From 502f5deba24d9125a7a23d406b6c0b9e78101ed8 Mon Sep 17 00:00:00 2001
From: Eric Czech <eric.allen.czech@gmail.com>
Date: Thu, 9 Nov 2023 15:00:54 -0500
Subject: [PATCH] Move examples notebook

---
 README.md               |    2 +-
 notebook/examples.ipynb | 2397 ---------------------------------------
 pyproject.toml          |    6 -
 3 files changed, 1 insertion(+), 2404 deletions(-)
 delete mode 100644 notebook/examples.ipynb

diff --git a/README.md b/README.md
index a8ea013..5dda1cc 100644
--- a/README.md
+++ b/README.md
@@ -66,4 +66,4 @@ ds
 <img src="doc/images/ds.png">
 
 
-Now it's convenient to access all this information via the generated `Dataset`, and some examples of this are in [notebook/examples.ipynb](notebook/examples.ipynb).
+Now it's convenient to access all this information via the generated `Dataset`, and some examples of this are in [examples.ipynb](doc/notebook/examples.ipynb).
diff --git a/notebook/examples.ipynb b/notebook/examples.ipynb
deleted file mode 100644
index a8cdda2..0000000
--- a/notebook/examples.ipynb
+++ /dev/null
@@ -1,2397 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "251cfc6f",
-   "metadata": {},
-   "source": [
-    "### Basic `xrml` examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "702165a5-33c6-4a53-bf1b-56bc09d95ecc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import shap\n",
-    "import xrml as xl\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.ensemble import GradientBoostingClassifier\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.datasets import load_breast_cancer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8b131c9b-24e0-4fe8-b079-0f1c3d8254bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 569 entries, 0 to 568\n",
-      "Data columns (total 34 columns):\n",
-      " #   Column                   Non-Null Count  Dtype  \n",
-      "---  ------                   --------------  -----  \n",
-      " 0   mean radius              569 non-null    float64\n",
-      " 1   mean texture             569 non-null    float64\n",
-      " 2   mean perimeter           569 non-null    float64\n",
-      " 3   mean area                569 non-null    float64\n",
-      " 4   mean smoothness          569 non-null    float64\n",
-      " 5   mean compactness         569 non-null    float64\n",
-      " 6   mean concavity           569 non-null    float64\n",
-      " 7   mean concave points      569 non-null    float64\n",
-      " 8   mean symmetry            569 non-null    float64\n",
-      " 9   mean fractal dimension   569 non-null    float64\n",
-      " 10  radius error             569 non-null    float64\n",
-      " 11  texture error            569 non-null    float64\n",
-      " 12  perimeter error          569 non-null    float64\n",
-      " 13  area error               569 non-null    float64\n",
-      " 14  smoothness error         569 non-null    float64\n",
-      " 15  compactness error        569 non-null    float64\n",
-      " 16  concavity error          569 non-null    float64\n",
-      " 17  concave points error     569 non-null    float64\n",
-      " 18  symmetry error           569 non-null    float64\n",
-      " 19  fractal dimension error  569 non-null    float64\n",
-      " 20  worst radius             569 non-null    float64\n",
-      " 21  worst texture            569 non-null    float64\n",
-      " 22  worst perimeter          569 non-null    float64\n",
-      " 23  worst area               569 non-null    float64\n",
-      " 24  worst smoothness         569 non-null    float64\n",
-      " 25  worst compactness        569 non-null    float64\n",
-      " 26  worst concavity          569 non-null    float64\n",
-      " 27  worst concave points     569 non-null    float64\n",
-      " 28  worst symmetry           569 non-null    float64\n",
-      " 29  worst fractal dimension  569 non-null    float64\n",
-      " 30  target                   569 non-null    int64  \n",
-      " 31  row_id                   569 non-null    object \n",
-      " 32  filename                 569 non-null    object \n",
-      " 33  descr                    569 non-null    object \n",
-      "dtypes: float64(30), int64(1), object(3)\n",
-      "memory usage: 151.3+ KB\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean radius</th>\n",
-       "      <th>mean texture</th>\n",
-       "      <th>mean perimeter</th>\n",
-       "      <th>mean area</th>\n",
-       "      <th>mean smoothness</th>\n",
-       "      <th>mean compactness</th>\n",
-       "      <th>mean concavity</th>\n",
-       "      <th>mean concave points</th>\n",
-       "      <th>mean symmetry</th>\n",
-       "      <th>mean fractal dimension</th>\n",
-       "      <th>...</th>\n",
-       "      <th>worst smoothness</th>\n",
-       "      <th>worst compactness</th>\n",
-       "      <th>worst concavity</th>\n",
-       "      <th>worst concave points</th>\n",
-       "      <th>worst symmetry</th>\n",
-       "      <th>worst fractal dimension</th>\n",
-       "      <th>target</th>\n",
-       "      <th>row_id</th>\n",
-       "      <th>filename</th>\n",
-       "      <th>descr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17.99</td>\n",
-       "      <td>10.38</td>\n",
-       "      <td>122.80</td>\n",
-       "      <td>1001.0</td>\n",
-       "      <td>0.11840</td>\n",
-       "      <td>0.27760</td>\n",
-       "      <td>0.3001</td>\n",
-       "      <td>0.14710</td>\n",
-       "      <td>0.2419</td>\n",
-       "      <td>0.07871</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1622</td>\n",
-       "      <td>0.6656</td>\n",
-       "      <td>0.7119</td>\n",
-       "      <td>0.2654</td>\n",
-       "      <td>0.4601</td>\n",
-       "      <td>0.11890</td>\n",
-       "      <td>0</td>\n",
-       "      <td>R000</td>\n",
-       "      <td>breast_cancer.csv</td>\n",
-       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20.57</td>\n",
-       "      <td>17.77</td>\n",
-       "      <td>132.90</td>\n",
-       "      <td>1326.0</td>\n",
-       "      <td>0.08474</td>\n",
-       "      <td>0.07864</td>\n",
-       "      <td>0.0869</td>\n",
-       "      <td>0.07017</td>\n",
-       "      <td>0.1812</td>\n",
-       "      <td>0.05667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1238</td>\n",
-       "      <td>0.1866</td>\n",
-       "      <td>0.2416</td>\n",
-       "      <td>0.1860</td>\n",
-       "      <td>0.2750</td>\n",
-       "      <td>0.08902</td>\n",
-       "      <td>0</td>\n",
-       "      <td>R001</td>\n",
-       "      <td>breast_cancer.csv</td>\n",
-       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.69</td>\n",
-       "      <td>21.25</td>\n",
-       "      <td>130.00</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>0.10960</td>\n",
-       "      <td>0.15990</td>\n",
-       "      <td>0.1974</td>\n",
-       "      <td>0.12790</td>\n",
-       "      <td>0.2069</td>\n",
-       "      <td>0.05999</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1444</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.4504</td>\n",
-       "      <td>0.2430</td>\n",
-       "      <td>0.3613</td>\n",
-       "      <td>0.08758</td>\n",
-       "      <td>0</td>\n",
-       "      <td>R002</td>\n",
-       "      <td>breast_cancer.csv</td>\n",
-       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.42</td>\n",
-       "      <td>20.38</td>\n",
-       "      <td>77.58</td>\n",
-       "      <td>386.1</td>\n",
-       "      <td>0.14250</td>\n",
-       "      <td>0.28390</td>\n",
-       "      <td>0.2414</td>\n",
-       "      <td>0.10520</td>\n",
-       "      <td>0.2597</td>\n",
-       "      <td>0.09744</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.2098</td>\n",
-       "      <td>0.8663</td>\n",
-       "      <td>0.6869</td>\n",
-       "      <td>0.2575</td>\n",
-       "      <td>0.6638</td>\n",
-       "      <td>0.17300</td>\n",
-       "      <td>0</td>\n",
-       "      <td>R003</td>\n",
-       "      <td>breast_cancer.csv</td>\n",
-       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.29</td>\n",
-       "      <td>14.34</td>\n",
-       "      <td>135.10</td>\n",
-       "      <td>1297.0</td>\n",
-       "      <td>0.10030</td>\n",
-       "      <td>0.13280</td>\n",
-       "      <td>0.1980</td>\n",
-       "      <td>0.10430</td>\n",
-       "      <td>0.1809</td>\n",
-       "      <td>0.05883</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.1374</td>\n",
-       "      <td>0.2050</td>\n",
-       "      <td>0.4000</td>\n",
-       "      <td>0.1625</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>R004</td>\n",
-       "      <td>breast_cancer.csv</td>\n",
-       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 34 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
-       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
-       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
-       "3        11.42         20.38           77.58      386.1          0.14250   \n",
-       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
-       "\n",
-       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-       "0           0.27760          0.3001              0.14710         0.2419   \n",
-       "1           0.07864          0.0869              0.07017         0.1812   \n",
-       "2           0.15990          0.1974              0.12790         0.2069   \n",
-       "3           0.28390          0.2414              0.10520         0.2597   \n",
-       "4           0.13280          0.1980              0.10430         0.1809   \n",
-       "\n",
-       "   mean fractal dimension  ...  worst smoothness  worst compactness  \\\n",
-       "0                 0.07871  ...            0.1622             0.6656   \n",
-       "1                 0.05667  ...            0.1238             0.1866   \n",
-       "2                 0.05999  ...            0.1444             0.4245   \n",
-       "3                 0.09744  ...            0.2098             0.8663   \n",
-       "4                 0.05883  ...            0.1374             0.2050   \n",
-       "\n",
-       "   worst concavity  worst concave points  worst symmetry  \\\n",
-       "0           0.7119                0.2654          0.4601   \n",
-       "1           0.2416                0.1860          0.2750   \n",
-       "2           0.4504                0.2430          0.3613   \n",
-       "3           0.6869                0.2575          0.6638   \n",
-       "4           0.4000                0.1625          0.2364   \n",
-       "\n",
-       "   worst fractal dimension  target  row_id           filename  \\\n",
-       "0                  0.11890       0    R000  breast_cancer.csv   \n",
-       "1                  0.08902       0    R001  breast_cancer.csv   \n",
-       "2                  0.08758       0    R002  breast_cancer.csv   \n",
-       "3                  0.17300       0    R003  breast_cancer.csv   \n",
-       "4                  0.07678       0    R004  breast_cancer.csv   \n",
-       "\n",
-       "                                               descr  \n",
-       "0  .. _breast_cancer_dataset:\\n\\nBreast cancer wi...  \n",
-       "1  .. _breast_cancer_dataset:\\n\\nBreast cancer wi...  \n",
-       "2  .. _breast_cancer_dataset:\\n\\nBreast cancer wi...  \n",
-       "3  .. _breast_cancer_dataset:\\n\\nBreast cancer wi...  \n",
-       "4  .. _breast_cancer_dataset:\\n\\nBreast cancer wi...  \n",
-       "\n",
-       "[5 rows x 34 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load a standard scikit-learn dataset with features and some binary outcome\n",
-    "data = load_breast_cancer()\n",
-    "\n",
-    "df = (\n",
-    "    pd.DataFrame(data['data'], columns=data['feature_names'])\n",
-    "    .assign(target=data['target'])\n",
-    "    .assign(row_id=lambda df: [f'R{i:03d}' for i in range(len(df))])\n",
-    "    .assign(filename=data['filename'])\n",
-    "    .assign(descr=data['DESCR'])\n",
-    ")\n",
-    "df.info()\n",
-    "df.head()"
-   ]
-  },
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-   "execution_count": 3,
-   "id": "49eb0cac-a664-4098-9022-3dc23490ae1a",
-   "metadata": {},
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-     "name": "stderr",
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-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:          (index: 569, features: 30, outcomes: 1, descriptors: 2,\n",
-       "                      folds: 5, estimators: 2, models: 2, classes: 2, scores: 24)\n",
-       "Coordinates:\n",
-       "  * index            (index) object &#x27;R000&#x27; &#x27;R001&#x27; &#x27;R002&#x27; ... &#x27;R567&#x27; &#x27;R568&#x27;\n",
-       "  * features         (features) &lt;U23 &#x27;mean radius&#x27; ... &#x27;worst fractal dimension&#x27;\n",
-       "  * outcomes         (outcomes) &lt;U6 &#x27;target&#x27;\n",
-       "  * descriptors      (descriptors) &lt;U8 &#x27;descr&#x27; &#x27;filename&#x27;\n",
-       "  * folds            (folds) int64 0 1 2 3 4\n",
-       "  * estimators       (estimators) &lt;U3 &#x27;lr&#x27; &#x27;gbm&#x27;\n",
-       "  * models           (models) &lt;U3 &#x27;gbm&#x27; &#x27;lr&#x27;\n",
-       "  * classes          (classes) &lt;U8 &#x27;negative&#x27; &#x27;positive&#x27;\n",
-       "  * scores           (scores) &lt;U17 &#x27;n_samples&#x27; &#x27;n_positives&#x27; ... &#x27;recall@1000&#x27;\n",
-       "Data variables:\n",
-       "    feature          (index, features) float64 17.99 10.38 ... 0.2871 0.07039\n",
-       "    outcome          (index, outcomes) int64 0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1\n",
-       "    descriptor       (index, descriptors) object &#x27;.. _breast_cancer_dataset:\\...\n",
-       "    fold             (index, folds) object &#x27;train&#x27; &#x27;train&#x27; ... &#x27;train&#x27; &#x27;train&#x27;\n",
-       "    estimator        (folds, estimators) object Model(slug=&#x27;lr&#x27;, name=&#x27;Logist...\n",
-       "    prediction       (index, classes, models, outcomes) float64 0.9961 ... 1.0\n",
-       "    model            (folds, models) object Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient ...\n",
-       "    score            (folds, outcomes, models, scores) float64 114.0 ... 1.0\n",
-       "    shap_feature     (models, index, features) float64 17.99 10.38 ... nan nan\n",
-       "    shap_value       (models, index, features) float64 0.004273 1.363 ... nan\n",
-       "    shap_base_value  (models, index) float64 1.75 2.146 2.522 ... nan nan nan\n",
-       "Attributes:\n",
-       "    outcome_type:  classification:binary</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c422ddf0-f723-4974-aee9-7ec912cb5f14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c422ddf0-f723-4974-aee9-7ec912cb5f14' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 569</li><li><span class='xr-has-index'>features</span>: 30</li><li><span class='xr-has-index'>outcomes</span>: 1</li><li><span class='xr-has-index'>descriptors</span>: 2</li><li><span class='xr-has-index'>folds</span>: 5</li><li><span class='xr-has-index'>estimators</span>: 2</li><li><span class='xr-has-index'>models</span>: 2</li><li><span class='xr-has-index'>classes</span>: 2</li><li><span class='xr-has-index'>scores</span>: 24</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dcacb1cc-3466-4bf2-afeb-4a3d7baf7a9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcacb1cc-3466-4bf2-afeb-4a3d7baf7a9c' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;R000&#x27; &#x27;R001&#x27; ... &#x27;R567&#x27; &#x27;R568&#x27;</div><input id='attrs-d494ad05-c706-465e-885a-b40b452c5b57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d494ad05-c706-465e-885a-b40b452c5b57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-674137fe-335e-4daa-b8c0-5f6ddb0a3a26' class='xr-var-data-in' type='checkbox'><label for='data-674137fe-335e-4daa-b8c0-5f6ddb0a3a26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R000&#x27;, &#x27;R001&#x27;, &#x27;R002&#x27;, ..., &#x27;R566&#x27;, &#x27;R567&#x27;, &#x27;R568&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>features</span></div><div class='xr-var-dims'>(features)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;mean radius&#x27; ... &#x27;worst fractal...</div><input id='attrs-057de7a5-05fc-45eb-acb8-7504b5f1e390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-057de7a5-05fc-45eb-acb8-7504b5f1e390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-061af491-b4f2-4187-886e-819e72ea1dd1' class='xr-var-data-in' type='checkbox'><label for='data-061af491-b4f2-4187-886e-819e72ea1dd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;mean radius&#x27;, &#x27;mean texture&#x27;, &#x27;mean perimeter&#x27;, &#x27;mean area&#x27;,\n",
-       "       &#x27;mean smoothness&#x27;, &#x27;mean compactness&#x27;, &#x27;mean concavity&#x27;,\n",
-       "       &#x27;mean concave points&#x27;, &#x27;mean symmetry&#x27;, &#x27;mean fractal dimension&#x27;,\n",
-       "       &#x27;radius error&#x27;, &#x27;texture error&#x27;, &#x27;perimeter error&#x27;, &#x27;area error&#x27;,\n",
-       "       &#x27;smoothness error&#x27;, &#x27;compactness error&#x27;, &#x27;concavity error&#x27;,\n",
-       "       &#x27;concave points error&#x27;, &#x27;symmetry error&#x27;, &#x27;fractal dimension error&#x27;,\n",
-       "       &#x27;worst radius&#x27;, &#x27;worst texture&#x27;, &#x27;worst perimeter&#x27;, &#x27;worst area&#x27;,\n",
-       "       &#x27;worst smoothness&#x27;, &#x27;worst compactness&#x27;, &#x27;worst concavity&#x27;,\n",
-       "       &#x27;worst concave points&#x27;, &#x27;worst symmetry&#x27;, &#x27;worst fractal dimension&#x27;],\n",
-       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>outcomes</span></div><div class='xr-var-dims'>(outcomes)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;target&#x27;</div><input id='attrs-15ac51c5-2ccd-41e3-b51f-1b74630fa269' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15ac51c5-2ccd-41e3-b51f-1b74630fa269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83cc0155-c5d8-4195-87ee-fbc67a4f5ec2' class='xr-var-data-in' type='checkbox'><label for='data-83cc0155-c5d8-4195-87ee-fbc67a4f5ec2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;target&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>descriptors</span></div><div class='xr-var-dims'>(descriptors)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;descr&#x27; &#x27;filename&#x27;</div><input id='attrs-206bec13-a128-43b2-88cd-327674856f3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-206bec13-a128-43b2-88cd-327674856f3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7278022f-b33b-4752-b727-55dc500f3ffa' class='xr-var-data-in' type='checkbox'><label for='data-7278022f-b33b-4752-b727-55dc500f3ffa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;descr&#x27;, &#x27;filename&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>folds</span></div><div class='xr-var-dims'>(folds)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-a7dd3a45-797a-463a-b35c-b2b0d0d87bac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7dd3a45-797a-463a-b35c-b2b0d0d87bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06078c26-f775-40d8-93d7-5fe53e5f35fc' class='xr-var-data-in' type='checkbox'><label for='data-06078c26-f775-40d8-93d7-5fe53e5f35fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>estimators</span></div><div class='xr-var-dims'>(estimators)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;lr&#x27; &#x27;gbm&#x27;</div><input id='attrs-37f10829-2fae-435c-9c65-df8294ac730f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37f10829-2fae-435c-9c65-df8294ac730f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92bcaf9c-2dea-4d53-a1e3-2d6d727a0bff' class='xr-var-data-in' type='checkbox'><label for='data-92bcaf9c-2dea-4d53-a1e3-2d6d727a0bff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;lr&#x27;, &#x27;gbm&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>models</span></div><div class='xr-var-dims'>(models)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;gbm&#x27; &#x27;lr&#x27;</div><input id='attrs-8c9d265d-d17b-4b2b-82a8-73d79b487b8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c9d265d-d17b-4b2b-82a8-73d79b487b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92c8a6e6-263f-44b0-a69e-73e6d540887d' class='xr-var-data-in' type='checkbox'><label for='data-92c8a6e6-263f-44b0-a69e-73e6d540887d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;gbm&#x27;, &#x27;lr&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>classes</span></div><div class='xr-var-dims'>(classes)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;negative&#x27; &#x27;positive&#x27;</div><input id='attrs-d57d7969-d244-458c-ab72-36e0d3f3f6af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d57d7969-d244-458c-ab72-36e0d3f3f6af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34f9fbea-1e83-4038-ae80-044b16788afa' class='xr-var-data-in' type='checkbox'><label for='data-34f9fbea-1e83-4038-ae80-044b16788afa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;negative&#x27;, &#x27;positive&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scores</span></div><div class='xr-var-dims'>(scores)</div><div class='xr-var-dtype'>&lt;U17</div><div class='xr-var-preview xr-preview'>&#x27;n_samples&#x27; ... &#x27;recall@1000&#x27;</div><input id='attrs-b8362053-1a0e-4ee3-9159-a8673e7d2365' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8362053-1a0e-4ee3-9159-a8673e7d2365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a21696-bcaf-4a0a-a109-c90965cef06a' class='xr-var-data-in' type='checkbox'><label for='data-22a21696-bcaf-4a0a-a109-c90965cef06a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;n_samples&#x27;, &#x27;n_positives&#x27;, &#x27;balance&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;,\n",
-       "       &#x27;mcc&#x27;, &#x27;roc_auc&#x27;, &#x27;average_precision&#x27;, &#x27;brier&#x27;, &#x27;balanced_mae&#x27;,\n",
-       "       &#x27;log_loss&#x27;, &#x27;precision@10&#x27;, &#x27;precision@50&#x27;, &#x27;precision@100&#x27;,\n",
-       "       &#x27;precision@200&#x27;, &#x27;precision@500&#x27;, &#x27;precision@1000&#x27;, &#x27;recall@10&#x27;,\n",
-       "       &#x27;recall@50&#x27;, &#x27;recall@100&#x27;, &#x27;recall@200&#x27;, &#x27;recall@500&#x27;, &#x27;recall@1000&#x27;],\n",
-       "      dtype=&#x27;&lt;U17&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75f38996-9a6d-4765-8a7f-6ff2d7c84ca1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75f38996-9a6d-4765-8a7f-6ff2d7c84ca1' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>feature</span></div><div class='xr-var-dims'>(index, features)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.99 10.38 ... 0.2871 0.07039</div><input id='attrs-944ca9a4-bbde-40b0-8a2a-ba2c4face1f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-944ca9a4-bbde-40b0-8a2a-ba2c4face1f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981fabad-3556-49e0-888c-561a9cad6457' class='xr-var-data-in' type='checkbox'><label for='data-981fabad-3556-49e0-888c-561a9cad6457' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
-       "        1.189e-01],\n",
-       "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
-       "        8.902e-02],\n",
-       "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
-       "        8.758e-02],\n",
-       "       ...,\n",
-       "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
-       "        7.820e-02],\n",
-       "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
-       "        1.240e-01],\n",
-       "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
-       "        7.039e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>outcome</span></div><div class='xr-var-dims'>(index, outcomes)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-5e0e793c-bd26-48e9-81f9-b8fc2659da88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e0e793c-bd26-48e9-81f9-b8fc2659da88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86b0a7af-68bd-4c04-8ff5-b1ad45b56f41' class='xr-var-data-in' type='checkbox'><label for='data-86b0a7af-68bd-4c04-8ff5-b1ad45b56f41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [1],\n",
-       "...\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [0],\n",
-       "       [1]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>descriptor</span></div><div class='xr-var-dims'>(index, descriptors)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;.. _breast_cancer_dataset:\\n\\nB...</div><input id='attrs-667078ab-e07d-481b-af09-20363b5f568e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-667078ab-e07d-481b-af09-20363b5f568e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-759a8be2-f5a3-48d1-8f73-d5a56da3d5fc' class='xr-var-data-in' type='checkbox'><label for='data-759a8be2-f5a3-48d1-8f73-d5a56da3d5fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;],\n",
-       "       [&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;],\n",
-       "       [&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;],\n",
-       "       ...,\n",
-       "       [&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;],\n",
-       "       [&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;],\n",
-       "       [&#x27;.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (&quot;coastline approximation&quot; - 1)\\n\\n        The mean, standard error, and &quot;worst&quot; or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, &quot;Decision Tree\\nConstruction Via Linear Programming.&quot; Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: &quot;Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets&quot;,\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n  for breast tumor diagnosis. IS&amp;T/SPIE 1993 International Symposium on \\n  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n  San Jose, CA, 1993.\\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n  prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n  July-August 1995.\\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n  163-171.\\n\\n|details-end|&#x27;,\n",
-       "        &#x27;breast_cancer.csv&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fold</span></div><div class='xr-var-dims'>(index, folds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;train&#x27; &#x27;train&#x27; ... &#x27;train&#x27; &#x27;train&#x27;</div><input id='attrs-fc0da52c-bc1f-490d-b3db-503641893097' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc0da52c-bc1f-490d-b3db-503641893097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5de9e503-d772-4661-9a30-0a4492a1087f' class='xr-var-data-in' type='checkbox'><label for='data-5de9e503-d772-4661-9a30-0a4492a1087f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;test&#x27;],\n",
-       "       [&#x27;test&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;],\n",
-       "       [&#x27;train&#x27;, &#x27;train&#x27;, &#x27;test&#x27;, &#x27;train&#x27;, &#x27;train&#x27;],\n",
-       "       ...,\n",
-       "       [&#x27;train&#x27;, &#x27;test&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;],\n",
-       "       [&#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;train&#x27;, &#x27;test&#x27;],\n",
-       "       [&#x27;train&#x27;, &#x27;train&#x27;, &#x27;test&#x27;, &#x27;train&#x27;, &#x27;train&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>estimator</span></div><div class='xr-var-dims'>(folds, estimators)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic ...</div><input id='attrs-9c2b365e-5cdf-4196-88dd-409e24f2d8fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c2b365e-5cdf-4196-88dd-409e24f2d8fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da94bbe7-e9f0-4e8b-8fdc-d849cceafb89' class='xr-var-data-in' type='checkbox'><label for='data-da94bbe7-e9f0-4e8b-8fdc-d849cceafb89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ,\n",
-       "        Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ],\n",
-       "       [Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ,\n",
-       "        Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ],\n",
-       "       [Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ,\n",
-       "        Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ],\n",
-       "       [Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ,\n",
-       "        Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ],\n",
-       "       [Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ,\n",
-       "        Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ]],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prediction</span></div><div class='xr-var-dims'>(index, classes, models, outcomes)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9961 1.0 0.003939 ... 0.999 1.0</div><input id='attrs-f18602bf-97ea-4487-86cc-5a973b60d647' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f18602bf-97ea-4487-86cc-5a973b60d647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46d80b07-7e65-4c12-b578-5496c153021c' class='xr-var-data-in' type='checkbox'><label for='data-46d80b07-7e65-4c12-b578-5496c153021c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[9.96061025e-01],\n",
-       "         [9.99999989e-01]],\n",
-       "\n",
-       "        [[3.93897528e-03],\n",
-       "         [1.07598218e-08]]],\n",
-       "\n",
-       "\n",
-       "       [[[9.98865191e-01],\n",
-       "         [9.99972871e-01]],\n",
-       "\n",
-       "        [[1.13480944e-03],\n",
-       "         [2.71291650e-05]]],\n",
-       "\n",
-       "\n",
-       "       [[[9.99553418e-01],\n",
-       "         [9.99999684e-01]],\n",
-       "\n",
-       "        [[4.46581530e-04],\n",
-       "         [3.15532081e-07]]],\n",
-       "\n",
-       "...\n",
-       "\n",
-       "       [[[9.97726295e-01],\n",
-       "         [9.84900415e-01]],\n",
-       "\n",
-       "        [[2.27370517e-03],\n",
-       "         [1.50995850e-02]]],\n",
-       "\n",
-       "\n",
-       "       [[[9.99567388e-01],\n",
-       "         [1.00000000e+00]],\n",
-       "\n",
-       "        [[4.32611914e-04],\n",
-       "         [1.02897316e-10]]],\n",
-       "\n",
-       "\n",
-       "       [[[9.61440362e-04],\n",
-       "         [1.81402364e-05]],\n",
-       "\n",
-       "        [[9.99038560e-01],\n",
-       "         [9.99981860e-01]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>(folds, models)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient...</div><input id='attrs-9b96d651-5636-4725-a6a0-b639f1c04b96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b96d651-5636-4725-a6a0-b639f1c04b96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99eb1d2b-9f64-45c4-b95c-166cccfd8e74' class='xr-var-data-in' type='checkbox'><label for='data-99eb1d2b-9f64-45c4-b95c-166cccfd8e74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ,\n",
-       "        Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ],\n",
-       "       [Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ,\n",
-       "        Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ],\n",
-       "       [Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ,\n",
-       "        Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ],\n",
-       "       [Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ,\n",
-       "        Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ],\n",
-       "       [Model(slug=&#x27;gbm&#x27;, name=&#x27;Gradient Boosting&#x27;, estimator=Pipeline(steps=[(&#x27;gradientboostingclassifier&#x27;,\n",
-       "                         GradientBoostingClassifier(random_state=0))]), properties={})                      ,\n",
-       "        Model(slug=&#x27;lr&#x27;, name=&#x27;Logistic Regression&#x27;, estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                        (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))]), properties={})                ]],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>score</span></div><div class='xr-var-dims'>(folds, outcomes, models, scores)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>114.0 71.0 0.6228 ... 1.0 1.0 1.0</div><input id='attrs-ea8d4b85-622e-4019-8f4f-de11fdd1b678' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea8d4b85-622e-4019-8f4f-de11fdd1b678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-863d19c9-9d3e-446d-867f-3523283ac06c' class='xr-var-data-in' type='checkbox'><label for='data-863d19c9-9d3e-446d-867f-3523283ac06c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1.14000000e+02, 7.10000000e+01, 6.22807018e-01,\n",
-       "          9.43661972e-01, 9.43661972e-01, 9.43661972e-01,\n",
-       "          8.50638716e-01, 9.79364559e-01, 9.85890125e-01,\n",
-       "          5.16666417e-02, 7.23966242e-02, 2.24523593e-01,\n",
-       "          1.00000000e+00, 9.80000000e-01, 7.10000000e-01,\n",
-       "          6.22807018e-01, 6.22807018e-01, 6.22807018e-01,\n",
-       "          1.40845070e-01, 6.90140845e-01, 1.00000000e+00,\n",
-       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
-       "         [1.14000000e+02, 7.10000000e+01, 6.22807018e-01,\n",
-       "          9.45945946e-01, 9.85915493e-01, 9.65517241e-01,\n",
-       "          9.06810612e-01, 9.84605306e-01, 9.89222542e-01,\n",
-       "          3.57514476e-02, 7.21045332e-02, 1.41322896e-01,\n",
-       "          1.00000000e+00, 9.80000000e-01, 7.10000000e-01,\n",
-       "          6.22807018e-01, 6.22807018e-01, 6.22807018e-01,\n",
-       "          1.40845070e-01, 6.90140845e-01, 1.00000000e+00,\n",
-       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "       [[[1.14000000e+02, 7.10000000e+01, 6.22807018e-01,\n",
-       "          1.00000000e+00, 9.71830986e-01, 9.85714286e-01,\n",
-       "...\n",
-       "          1.38888889e-01, 6.94444444e-01, 1.00000000e+00,\n",
-       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "       [[[1.13000000e+02, 7.10000000e+01, 6.28318584e-01,\n",
-       "          9.72222222e-01, 9.85915493e-01, 9.79020979e-01,\n",
-       "          9.43052160e-01, 9.96646546e-01, 9.98015479e-01,\n",
-       "          2.12437946e-02, 3.83608610e-02, 7.69304059e-02,\n",
-       "          1.00000000e+00, 1.00000000e+00, 7.10000000e-01,\n",
-       "          6.28318584e-01, 6.28318584e-01, 6.28318584e-01,\n",
-       "          1.40845070e-01, 7.04225352e-01, 1.00000000e+00,\n",
-       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
-       "         [1.13000000e+02, 7.10000000e+01, 6.28318584e-01,\n",
-       "          9.85915493e-01, 9.85915493e-01, 9.85915493e-01,\n",
-       "          9.62105969e-01, 9.95640510e-01, 9.97261294e-01,\n",
-       "          1.62172595e-02, 4.13466723e-02, 6.27189065e-02,\n",
-       "          1.00000000e+00, 1.00000000e+00, 7.10000000e-01,\n",
-       "          6.28318584e-01, 6.28318584e-01, 6.28318584e-01,\n",
-       "          1.40845070e-01, 7.04225352e-01, 1.00000000e+00,\n",
-       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shap_feature</span></div><div class='xr-var-dims'>(models, index, features)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.99 10.38 122.8 ... nan nan nan</div><input id='attrs-ee66e867-f753-45e6-9a34-c2abee5a980e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee66e867-f753-45e6-9a34-c2abee5a980e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6129977b-4424-4644-a089-d1915c65ff6c' class='xr-var-data-in' type='checkbox'><label for='data-6129977b-4424-4644-a089-d1915c65ff6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
-       "         1.189e-01],\n",
-       "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
-       "         8.902e-02],\n",
-       "        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
-       "         8.758e-02],\n",
-       "        ...,\n",
-       "        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
-       "         7.820e-02],\n",
-       "        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
-       "         1.240e-01],\n",
-       "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
-       "         7.039e-02]],\n",
-       "\n",
-       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        ...,\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shap_value</span></div><div class='xr-var-dims'>(models, index, features)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.004273 1.363 0.05967 ... nan nan</div><input id='attrs-34c0176a-2f41-4707-8b09-b7adef96d41e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34c0176a-2f41-4707-8b09-b7adef96d41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa932325-593f-407d-bd39-29427893c067' class='xr-var-data-in' type='checkbox'><label for='data-aa932325-593f-407d-bd39-29427893c067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 4.27339105e-03,  1.36287735e+00,  5.96699769e-02, ...,\n",
-       "         -2.53790135e+00, -2.24458420e-01, -3.50359203e-04],\n",
-       "        [-1.75753553e-02,  6.00081508e-02,  1.78924253e-01, ...,\n",
-       "         -1.80742278e+00,  1.52063567e-01,  0.00000000e+00],\n",
-       "        [ 8.21273411e-02, -3.78293196e-01,  1.45663444e-01, ...,\n",
-       "         -1.56128996e+00, -8.06143127e-02, -3.66226379e-04],\n",
-       "        ...,\n",
-       "        [-6.95853824e-02, -4.69875671e-01,  1.91043138e-01, ...,\n",
-       "         -3.15351268e-02,  3.06703020e-02,  0.00000000e+00],\n",
-       "        [ 4.27339105e-03, -6.66876926e-01,  7.24708635e-03, ...,\n",
-       "         -1.94636679e+00, -2.62362625e-02, -3.50359203e-04],\n",
-       "        [-4.39116843e-02, -3.51474625e-01, -5.92475088e-02, ...,\n",
-       "          8.36085541e-01,  3.82151376e-02,  1.77504135e-02]],\n",
-       "\n",
-       "       [[            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shap_base_value</span></div><div class='xr-var-dims'>(models, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.75 2.146 2.522 ... nan nan nan</div><input id='attrs-f5e53dc7-3018-483e-8ab2-a4ae1fc0f35c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5e53dc7-3018-483e-8ab2-a4ae1fc0f35c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5fe97aa-d8ce-43a9-a030-aa909e3726b2' class='xr-var-data-in' type='checkbox'><label for='data-c5fe97aa-d8ce-43a9-a030-aa909e3726b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.74965507, 2.14618236, 2.52239375, ..., 1.4068675 , 1.74965507,\n",
-       "        2.52239375],\n",
-       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-858e0973-5dfc-4ec9-9d19-4f3a04c19632' class='xr-section-summary-in' type='checkbox'  ><label for='section-858e0973-5dfc-4ec9-9d19-4f3a04c19632' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbd487e8-0b62-41a8-b63e-4acfff4128b8' class='xr-index-data-in' type='checkbox'/><label for='index-cbd487e8-0b62-41a8-b63e-4acfff4128b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;R000&#x27;, &#x27;R001&#x27;, &#x27;R002&#x27;, &#x27;R003&#x27;, &#x27;R004&#x27;, &#x27;R005&#x27;, &#x27;R006&#x27;, &#x27;R007&#x27;, &#x27;R008&#x27;,\n",
-       "       &#x27;R009&#x27;,\n",
-       "       ...\n",
-       "       &#x27;R559&#x27;, &#x27;R560&#x27;, &#x27;R561&#x27;, &#x27;R562&#x27;, &#x27;R563&#x27;, &#x27;R564&#x27;, &#x27;R565&#x27;, &#x27;R566&#x27;, &#x27;R567&#x27;,\n",
-       "       &#x27;R568&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;index&#x27;, length=569))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>features</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b02e0e8d-5d8c-4ff1-bb92-d0fac21f4364' class='xr-index-data-in' type='checkbox'/><label for='index-b02e0e8d-5d8c-4ff1-bb92-d0fac21f4364' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;mean radius&#x27;, &#x27;mean texture&#x27;, &#x27;mean perimeter&#x27;, &#x27;mean area&#x27;,\n",
-       "       &#x27;mean smoothness&#x27;, &#x27;mean compactness&#x27;, &#x27;mean concavity&#x27;,\n",
-       "       &#x27;mean concave points&#x27;, &#x27;mean symmetry&#x27;, &#x27;mean fractal dimension&#x27;,\n",
-       "       &#x27;radius error&#x27;, &#x27;texture error&#x27;, &#x27;perimeter error&#x27;, &#x27;area error&#x27;,\n",
-       "       &#x27;smoothness error&#x27;, &#x27;compactness error&#x27;, &#x27;concavity error&#x27;,\n",
-       "       &#x27;concave points error&#x27;, &#x27;symmetry error&#x27;, &#x27;fractal dimension error&#x27;,\n",
-       "       &#x27;worst radius&#x27;, &#x27;worst texture&#x27;, &#x27;worst perimeter&#x27;, &#x27;worst area&#x27;,\n",
-       "       &#x27;worst smoothness&#x27;, &#x27;worst compactness&#x27;, &#x27;worst concavity&#x27;,\n",
-       "       &#x27;worst concave points&#x27;, &#x27;worst symmetry&#x27;, &#x27;worst fractal dimension&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;features&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>outcomes</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a799ead1-f021-446b-b895-aab6794d89cb' class='xr-index-data-in' type='checkbox'/><label for='index-a799ead1-f021-446b-b895-aab6794d89cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;target&#x27;], dtype=&#x27;object&#x27;, name=&#x27;outcomes&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>descriptors</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9489ed73-d118-450a-9e40-86080a2552c2' class='xr-index-data-in' type='checkbox'/><label for='index-9489ed73-d118-450a-9e40-86080a2552c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;descr&#x27;, &#x27;filename&#x27;], dtype=&#x27;object&#x27;, name=&#x27;descriptors&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>folds</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e91b58da-8210-4547-bd63-633bc69b2e35' class='xr-index-data-in' type='checkbox'/><label for='index-e91b58da-8210-4547-bd63-633bc69b2e35' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;folds&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>estimators</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6aaa4cf4-62f3-43ee-abc6-a6b6c1079628' class='xr-index-data-in' type='checkbox'/><label for='index-6aaa4cf4-62f3-43ee-abc6-a6b6c1079628' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;lr&#x27;, &#x27;gbm&#x27;], dtype=&#x27;object&#x27;, name=&#x27;estimators&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>models</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec972e7d-5a82-4ef3-8d91-33527129373f' class='xr-index-data-in' type='checkbox'/><label for='index-ec972e7d-5a82-4ef3-8d91-33527129373f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;gbm&#x27;, &#x27;lr&#x27;], dtype=&#x27;object&#x27;, name=&#x27;models&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>classes</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34746af8-c719-40d4-a949-1c8c08d6c3fa' class='xr-index-data-in' type='checkbox'/><label for='index-34746af8-c719-40d4-a949-1c8c08d6c3fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;negative&#x27;, &#x27;positive&#x27;], dtype=&#x27;object&#x27;, name=&#x27;classes&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>scores</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e0f8942-a851-4e2d-a0e8-8cf00dd01853' class='xr-index-data-in' type='checkbox'/><label for='index-8e0f8942-a851-4e2d-a0e8-8cf00dd01853' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;n_samples&#x27;, &#x27;n_positives&#x27;, &#x27;balance&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;,\n",
-       "       &#x27;mcc&#x27;, &#x27;roc_auc&#x27;, &#x27;average_precision&#x27;, &#x27;brier&#x27;, &#x27;balanced_mae&#x27;,\n",
-       "       &#x27;log_loss&#x27;, &#x27;precision@10&#x27;, &#x27;precision@50&#x27;, &#x27;precision@100&#x27;,\n",
-       "       &#x27;precision@200&#x27;, &#x27;precision@500&#x27;, &#x27;precision@1000&#x27;, &#x27;recall@10&#x27;,\n",
-       "       &#x27;recall@50&#x27;, &#x27;recall@100&#x27;, &#x27;recall@200&#x27;, &#x27;recall@500&#x27;, &#x27;recall@1000&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;scores&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6aac156a-dee0-41c1-b9b8-c9f42af78163' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6aac156a-dee0-41c1-b9b8-c9f42af78163' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>outcome_type :</span></dt><dd>classification:binary</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:          (index: 569, features: 30, outcomes: 1, descriptors: 2,\n",
-       "                      folds: 5, estimators: 2, models: 2, classes: 2, scores: 24)\n",
-       "Coordinates:\n",
-       "  * index            (index) object 'R000' 'R001' 'R002' ... 'R567' 'R568'\n",
-       "  * features         (features) <U23 'mean radius' ... 'worst fractal dimension'\n",
-       "  * outcomes         (outcomes) <U6 'target'\n",
-       "  * descriptors      (descriptors) <U8 'descr' 'filename'\n",
-       "  * folds            (folds) int64 0 1 2 3 4\n",
-       "  * estimators       (estimators) <U3 'lr' 'gbm'\n",
-       "  * models           (models) <U3 'gbm' 'lr'\n",
-       "  * classes          (classes) <U8 'negative' 'positive'\n",
-       "  * scores           (scores) <U17 'n_samples' 'n_positives' ... 'recall@1000'\n",
-       "Data variables:\n",
-       "    feature          (index, features) float64 17.99 10.38 ... 0.2871 0.07039\n",
-       "    outcome          (index, outcomes) int64 0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1\n",
-       "    descriptor       (index, descriptors) object '.. _breast_cancer_dataset:\\...\n",
-       "    fold             (index, folds) object 'train' 'train' ... 'train' 'train'\n",
-       "    estimator        (folds, estimators) object Model(slug='lr', name='Logist...\n",
-       "    prediction       (index, classes, models, outcomes) float64 0.9961 ... 1.0\n",
-       "    model            (folds, models) object Model(slug='gbm', name='Gradient ...\n",
-       "    score            (folds, outcomes, models, scores) float64 114.0 ... 1.0\n",
-       "    shap_feature     (models, index, features) float64 17.99 10.38 ... nan nan\n",
-       "    shap_value       (models, index, features) float64 0.004273 1.363 ... nan\n",
-       "    shap_base_value  (models, index) float64 1.75 2.146 2.522 ... nan nan nan\n",
-       "Attributes:\n",
-       "    outcome_type:  classification:binary"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Convert this to Xarray via `xrml`\n",
-    "ds = (\n",
-    "    df\n",
-    "    .pipe(\n",
-    "        xl.create_dataset,\n",
-    "        # Index rows using the generated row_id\n",
-    "        index=['row_id'], \n",
-    "        # Choose columns that contain outcomes\n",
-    "        outcomes=['target'],\n",
-    "        # Set some fields that provided metadata about training\n",
-    "        # samples without needing to push them into an index\n",
-    "        descriptors=['filename', 'descr']\n",
-    "    )\n",
-    "    # Set CV strategy\n",
-    "    .pipe(xl.add_stratified_splits, n_splits=5)\n",
-    "    # Choose estimators\n",
-    "    .pipe(xl.add_models, estimator_fn=lambda: [\n",
-    "        xl.Model(\n",
-    "            'lr', 'Logistic Regression', \n",
-    "            make_pipeline(StandardScaler(), LogisticRegression(random_state=0))\n",
-    "            .set_output(transform=\"pandas\")\n",
-    "        ),\n",
-    "        xl.Model(\n",
-    "            'gbm', 'Gradient Boosting',\n",
-    "            make_pipeline(GradientBoostingClassifier(random_state=0))\n",
-    "            .set_output(transform=\"pandas\")\n",
-    "        )\n",
-    "    ])\n",
-    "    # Run cross-validation\n",
-    "    .pipe(xl.run_cv, predict_fn=xl.predict_proba, score_fn=xl.score_classification)\n",
-    "    # Add SHAP explanations\n",
-    "    .pipe(xl.explain, model='gbm', tree_shap_check_additivity=False)\n",
-    ")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53316857-1944-43b8-b9b5-54243720e752",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_b08df_row0_col0, #T_b08df_row0_col1 {\n",
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-       "#T_b08df_row1_col0 {\n",
-       "  background-color: #a1bbda;\n",
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-       "#T_b08df_row1_col1 {\n",
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-       "#T_b08df_row3_col1 {\n",
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-       "#T_b08df_row4_col1 {\n",
-       "  background-color: #0d75b3;\n",
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-       "  background-color: #71a8ce;\n",
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-       "  background-color: #023858;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_b08df_row8_col1 {\n",
-       "  background-color: #046097;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_b08df_row9_col0 {\n",
-       "  background-color: #056faf;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_b08df_row9_col1 {\n",
-       "  background-color: #0569a5;\n",
-       "  color: #f1f1f1;\n",
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-       "</style>\n",
-       "<table id=\"T_b08df\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank\" >&nbsp;</th>\n",
-       "      <th class=\"blank\" >&nbsp;</th>\n",
-       "      <th class=\"index_name level0\" >scores</th>\n",
-       "      <th id=\"T_b08df_level0_col0\" class=\"col_heading level0 col0\" >f1</th>\n",
-       "      <th id=\"T_b08df_level0_col1\" class=\"col_heading level0 col1\" >roc_auc</th>\n",
-       "      <th id=\"T_b08df_level0_col2\" class=\"col_heading level0 col2\" >n_samples</th>\n",
-       "      <th id=\"T_b08df_level0_col3\" class=\"col_heading level0 col3\" >balance</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >folds</th>\n",
-       "      <th class=\"index_name level1\" >outcomes</th>\n",
-       "      <th class=\"index_name level2\" >models</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">0</th>\n",
-       "      <th id=\"T_b08df_level1_row0\" class=\"row_heading level1 row0\" rowspan=\"2\">target</th>\n",
-       "      <th id=\"T_b08df_level2_row0\" class=\"row_heading level2 row0\" >gbm</th>\n",
-       "      <td id=\"T_b08df_row0_col0\" class=\"data row0 col0\" >0.944</td>\n",
-       "      <td id=\"T_b08df_row0_col1\" class=\"data row0 col1\" >0.979</td>\n",
-       "      <td id=\"T_b08df_row0_col2\" class=\"data row0 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row0_col3\" class=\"data row0 col3\" >0.623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level2_row1\" class=\"row_heading level2 row1\" >lr</th>\n",
-       "      <td id=\"T_b08df_row1_col0\" class=\"data row1 col0\" >0.966</td>\n",
-       "      <td id=\"T_b08df_row1_col1\" class=\"data row1 col1\" >0.985</td>\n",
-       "      <td id=\"T_b08df_row1_col2\" class=\"data row1 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row1_col3\" class=\"data row1 col3\" >0.623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"2\">1</th>\n",
-       "      <th id=\"T_b08df_level1_row2\" class=\"row_heading level1 row2\" rowspan=\"2\">target</th>\n",
-       "      <th id=\"T_b08df_level2_row2\" class=\"row_heading level2 row2\" >gbm</th>\n",
-       "      <td id=\"T_b08df_row2_col0\" class=\"data row2 col0\" >0.986</td>\n",
-       "      <td id=\"T_b08df_row2_col1\" class=\"data row2 col1\" >0.998</td>\n",
-       "      <td id=\"T_b08df_row2_col2\" class=\"data row2 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row2_col3\" class=\"data row2 col3\" >0.623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level2_row3\" class=\"row_heading level2 row3\" >lr</th>\n",
-       "      <td id=\"T_b08df_row3_col0\" class=\"data row3 col0\" >0.979</td>\n",
-       "      <td id=\"T_b08df_row3_col1\" class=\"data row3 col1\" >0.999</td>\n",
-       "      <td id=\"T_b08df_row3_col2\" class=\"data row3 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row3_col3\" class=\"data row3 col3\" >0.623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"2\">2</th>\n",
-       "      <th id=\"T_b08df_level1_row4\" class=\"row_heading level1 row4\" rowspan=\"2\">target</th>\n",
-       "      <th id=\"T_b08df_level2_row4\" class=\"row_heading level2 row4\" >gbm</th>\n",
-       "      <td id=\"T_b08df_row4_col0\" class=\"data row4 col0\" >0.980</td>\n",
-       "      <td id=\"T_b08df_row4_col1\" class=\"data row4 col1\" >0.994</td>\n",
-       "      <td id=\"T_b08df_row4_col2\" class=\"data row4 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row4_col3\" class=\"data row4 col3\" >0.632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level2_row5\" class=\"row_heading level2 row5\" >lr</th>\n",
-       "      <td id=\"T_b08df_row5_col0\" class=\"data row5 col0\" >0.986</td>\n",
-       "      <td id=\"T_b08df_row5_col1\" class=\"data row5 col1\" >0.998</td>\n",
-       "      <td id=\"T_b08df_row5_col2\" class=\"data row5 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row5_col3\" class=\"data row5 col3\" >0.632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"2\">3</th>\n",
-       "      <th id=\"T_b08df_level1_row6\" class=\"row_heading level1 row6\" rowspan=\"2\">target</th>\n",
-       "      <th id=\"T_b08df_level2_row6\" class=\"row_heading level2 row6\" >gbm</th>\n",
-       "      <td id=\"T_b08df_row6_col0\" class=\"data row6 col0\" >0.972</td>\n",
-       "      <td id=\"T_b08df_row6_col1\" class=\"data row6 col1\" >0.996</td>\n",
-       "      <td id=\"T_b08df_row6_col2\" class=\"data row6 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row6_col3\" class=\"data row6 col3\" >0.632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level2_row7\" class=\"row_heading level2 row7\" >lr</th>\n",
-       "      <td id=\"T_b08df_row7_col0\" class=\"data row7 col0\" >1.000</td>\n",
-       "      <td id=\"T_b08df_row7_col1\" class=\"data row7 col1\" >1.000</td>\n",
-       "      <td id=\"T_b08df_row7_col2\" class=\"data row7 col2\" >114</td>\n",
-       "      <td id=\"T_b08df_row7_col3\" class=\"data row7 col3\" >0.632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"2\">4</th>\n",
-       "      <th id=\"T_b08df_level1_row8\" class=\"row_heading level1 row8\" rowspan=\"2\">target</th>\n",
-       "      <th id=\"T_b08df_level2_row8\" class=\"row_heading level2 row8\" >gbm</th>\n",
-       "      <td id=\"T_b08df_row8_col0\" class=\"data row8 col0\" >0.979</td>\n",
-       "      <td id=\"T_b08df_row8_col1\" class=\"data row8 col1\" >0.997</td>\n",
-       "      <td id=\"T_b08df_row8_col2\" class=\"data row8 col2\" >113</td>\n",
-       "      <td id=\"T_b08df_row8_col3\" class=\"data row8 col3\" >0.628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b08df_level2_row9\" class=\"row_heading level2 row9\" >lr</th>\n",
-       "      <td id=\"T_b08df_row9_col0\" class=\"data row9 col0\" >0.986</td>\n",
-       "      <td id=\"T_b08df_row9_col1\" class=\"data row9 col1\" >0.996</td>\n",
-       "      <td id=\"T_b08df_row9_col2\" class=\"data row9 col2\" >113</td>\n",
-       "      <td id=\"T_b08df_row9_col3\" class=\"data row9 col3\" >0.628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13fdeb5b0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show performance statistics across folds:\n",
-    "(\n",
-    "    ds.score.to_dataframe()\n",
-    "    .unstack('scores').droplevel(0, axis=1)\n",
-    "    [['f1', 'roc_auc', 'n_samples', 'balance']]\n",
-    "    .style\n",
-    "    .format(precision=3)\n",
-    "    .format(precision=0, subset='n_samples')\n",
-    "    .background_gradient(subset=['f1', 'roc_auc'])\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "02efd9fd-b065-4b30-9664-d076c2a6c6ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x550 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize SHAP distributions:\n",
-    "shap.summary_plot(\n",
-    "    ds.shap_value.sel(models='gbm').values, \n",
-    "    features=ds.shap_feature.sel(models='gbm').values, \n",
-    "    feature_names=ds.features.values, \n",
-    "    max_display=10\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "fcfa2c55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  background-color: #ffffbe;\n",
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-       "  background-color: #feeda1;\n",
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-       "#T_26958_row10_col1 {\n",
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-       "}\n",
-       "#T_26958_row10_col3 {\n",
-       "  background-color: #fffab6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row10_col4, #T_26958_row14_col3 {\n",
-       "  background-color: #fee695;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row10_col5 {\n",
-       "  background-color: #fff8b4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row11_col0 {\n",
-       "  background-color: #f8fcb4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row11_col1, #T_26958_row15_col5, #T_26958_row16_col5 {\n",
-       "  background-color: #fed481;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row11_col3, #T_26958_row12_col1, #T_26958_row12_col3, #T_26958_row13_col1 {\n",
-       "  background-color: #feec9f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row11_col4, #T_26958_row13_col2, #T_26958_row13_col3 {\n",
-       "  background-color: #feeb9d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row11_col5, #T_26958_row12_col2 {\n",
-       "  background-color: #fff0a6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row12_col0, #T_26958_row17_col2 {\n",
-       "  background-color: #fca85e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row12_col4, #T_26958_row16_col3 {\n",
-       "  background-color: #fee593;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row12_col5 {\n",
-       "  background-color: #fee28f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row13_col0, #T_26958_row17_col1 {\n",
-       "  background-color: #fca55d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row13_col4, #T_26958_row15_col2, #T_26958_row16_col1 {\n",
-       "  background-color: #fee491;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row13_col5, #T_26958_row15_col4 {\n",
-       "  background-color: #fee18d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row14_col0, #T_26958_row14_col1 {\n",
-       "  background-color: #fdbd6d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row14_col2, #T_26958_row18_col2, #T_26958_row18_col3 {\n",
-       "  background-color: #fdc574;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row14_col4 {\n",
-       "  background-color: #f9fcb5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row14_col5 {\n",
-       "  background-color: #feda86;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row15_col0, #T_26958_row17_col5, #T_26958_row18_col4 {\n",
-       "  background-color: #fba35c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row15_col1 {\n",
-       "  background-color: #fed884;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row15_col3 {\n",
-       "  background-color: #fee08b;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row16_col0 {\n",
-       "  background-color: #fdbf6f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row16_col2 {\n",
-       "  background-color: #fecc7b;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row16_col4, #T_26958_row22_col3 {\n",
-       "  background-color: #feca79;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row17_col0, #T_26958_row22_col0, #T_26958_row22_col5, #T_26958_row24_col1 {\n",
-       "  background-color: #f47044;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row17_col3 {\n",
-       "  background-color: #fedc88;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row17_col4, #T_26958_row22_col2 {\n",
-       "  background-color: #f8864f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row18_col0, #T_26958_row24_col3 {\n",
-       "  background-color: #c52c4b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row18_col1 {\n",
-       "  background-color: #fdbb6c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row18_col5 {\n",
-       "  background-color: #fb9d59;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row19_col0, #T_26958_row21_col0 {\n",
-       "  background-color: #f06744;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row19_col1 {\n",
-       "  background-color: #f88950;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row19_col2, #T_26958_row19_col3 {\n",
-       "  background-color: #f67a49;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row19_col4, #T_26958_row20_col0 {\n",
-       "  background-color: #f88c51;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row19_col5, #T_26958_row20_col5 {\n",
-       "  background-color: #f67c4a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row20_col1 {\n",
-       "  background-color: #ea5e47;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row20_col2 {\n",
-       "  background-color: #fba05b;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row20_col3, #T_26958_row27_col4 {\n",
-       "  background-color: #f26944;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row20_col4, #T_26958_row25_col3 {\n",
-       "  background-color: #f67f4b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row21_col1 {\n",
-       "  background-color: #f99355;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row21_col2 {\n",
-       "  background-color: #f98e52;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row21_col3 {\n",
-       "  background-color: #ee6445;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row21_col4, #T_26958_row25_col2, #T_26958_row26_col5, #T_26958_row27_col1 {\n",
-       "  background-color: #e95c47;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row21_col5 {\n",
-       "  background-color: #f57245;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row22_col1 {\n",
-       "  background-color: #e2514a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row22_col4 {\n",
-       "  background-color: #c72e4c;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row23_col0 {\n",
-       "  background-color: #f99153;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row23_col1, #T_26958_row25_col5 {\n",
-       "  background-color: #eb6046;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row23_col2 {\n",
-       "  background-color: #f57748;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row23_col3, #T_26958_row25_col0, #T_26958_row26_col0 {\n",
-       "  background-color: #e3534a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row23_col4, #T_26958_row24_col5, #T_26958_row25_col4 {\n",
-       "  background-color: #ef6645;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row23_col5, #T_26958_row24_col4 {\n",
-       "  background-color: #f36b43;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row24_col0 {\n",
-       "  background-color: #fcaa5f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_26958_row24_col2, #T_26958_row26_col3 {\n",
-       "  background-color: #e85b48;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row25_col1 {\n",
-       "  background-color: #e1504b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row26_col1 {\n",
-       "  background-color: #e55749;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row26_col2 {\n",
-       "  background-color: #e75948;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row26_col4 {\n",
-       "  background-color: #f46d43;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row27_col0 {\n",
-       "  background-color: #de4c4b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row27_col2 {\n",
-       "  background-color: #df4e4b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row27_col3 {\n",
-       "  background-color: #dc484c;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row27_col5 {\n",
-       "  background-color: #e45549;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row28_col0, #T_26958_row28_col1 {\n",
-       "  background-color: #be254a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row28_col2 {\n",
-       "  background-color: #a70b44;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row28_col3 {\n",
-       "  background-color: #cb334d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row28_col4, #T_26958_row29_col5 {\n",
-       "  background-color: #ba2049;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row28_col5 {\n",
-       "  background-color: #bc2249;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row29_col0 {\n",
-       "  background-color: #d0384e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row29_col1 {\n",
-       "  background-color: #c1274a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row29_col2 {\n",
-       "  background-color: #d43d4f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row29_col3 {\n",
-       "  background-color: #9e0142;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_26958_row29_col4 {\n",
-       "  background-color: #a40844;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_26958\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >folds</th>\n",
-       "      <th id=\"T_26958_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
-       "      <th id=\"T_26958_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
-       "      <th id=\"T_26958_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
-       "      <th id=\"T_26958_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
-       "      <th id=\"T_26958_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
-       "      <th id=\"T_26958_level0_col5\" class=\"col_heading level0 col5\" >mean</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >feature</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row0\" class=\"row_heading level0 row0\" >compactness error</th>\n",
-       "      <td id=\"T_26958_row0_col0\" class=\"data row0 col0\" >0.78</td>\n",
-       "      <td id=\"T_26958_row0_col1\" class=\"data row0 col1\" >0.97</td>\n",
-       "      <td id=\"T_26958_row0_col2\" class=\"data row0 col2\" >0.51</td>\n",
-       "      <td id=\"T_26958_row0_col3\" class=\"data row0 col3\" >0.69</td>\n",
-       "      <td id=\"T_26958_row0_col4\" class=\"data row0 col4\" >0.68</td>\n",
-       "      <td id=\"T_26958_row0_col5\" class=\"data row0 col5\" >0.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row1\" class=\"row_heading level0 row1\" >fractal dimension error</th>\n",
-       "      <td id=\"T_26958_row1_col0\" class=\"data row1 col0\" >0.84</td>\n",
-       "      <td id=\"T_26958_row1_col1\" class=\"data row1 col1\" >0.83</td>\n",
-       "      <td id=\"T_26958_row1_col2\" class=\"data row1 col2\" >0.53</td>\n",
-       "      <td id=\"T_26958_row1_col3\" class=\"data row1 col3\" >0.52</td>\n",
-       "      <td id=\"T_26958_row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
-       "      <td id=\"T_26958_row1_col5\" class=\"data row1 col5\" >0.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row2\" class=\"row_heading level0 row2\" >mean compactness</th>\n",
-       "      <td id=\"T_26958_row2_col0\" class=\"data row2 col0\" >0.09</td>\n",
-       "      <td id=\"T_26958_row2_col1\" class=\"data row2 col1\" >0.65</td>\n",
-       "      <td id=\"T_26958_row2_col2\" class=\"data row2 col2\" >0.44</td>\n",
-       "      <td id=\"T_26958_row2_col3\" class=\"data row2 col3\" >0.56</td>\n",
-       "      <td id=\"T_26958_row2_col4\" class=\"data row2 col4\" >0.61</td>\n",
-       "      <td id=\"T_26958_row2_col5\" class=\"data row2 col5\" >0.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row3\" class=\"row_heading level0 row3\" >mean fractal dimension</th>\n",
-       "      <td id=\"T_26958_row3_col0\" class=\"data row3 col0\" >0.42</td>\n",
-       "      <td id=\"T_26958_row3_col1\" class=\"data row3 col1\" >0.08</td>\n",
-       "      <td id=\"T_26958_row3_col2\" class=\"data row3 col2\" >0.34</td>\n",
-       "      <td id=\"T_26958_row3_col3\" class=\"data row3 col3\" >0.29</td>\n",
-       "      <td id=\"T_26958_row3_col4\" class=\"data row3 col4\" >0.44</td>\n",
-       "      <td id=\"T_26958_row3_col5\" class=\"data row3 col5\" >0.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row4\" class=\"row_heading level0 row4\" >symmetry error</th>\n",
-       "      <td id=\"T_26958_row4_col0\" class=\"data row4 col0\" >0.14</td>\n",
-       "      <td id=\"T_26958_row4_col1\" class=\"data row4 col1\" >0.36</td>\n",
-       "      <td id=\"T_26958_row4_col2\" class=\"data row4 col2\" >0.56</td>\n",
-       "      <td id=\"T_26958_row4_col3\" class=\"data row4 col3\" >0.16</td>\n",
-       "      <td id=\"T_26958_row4_col4\" class=\"data row4 col4\" >0.27</td>\n",
-       "      <td id=\"T_26958_row4_col5\" class=\"data row4 col5\" >0.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row5\" class=\"row_heading level0 row5\" >texture error</th>\n",
-       "      <td id=\"T_26958_row5_col0\" class=\"data row5 col0\" >0.13</td>\n",
-       "      <td id=\"T_26958_row5_col1\" class=\"data row5 col1\" >0.23</td>\n",
-       "      <td id=\"T_26958_row5_col2\" class=\"data row5 col2\" >0.26</td>\n",
-       "      <td id=\"T_26958_row5_col3\" class=\"data row5 col3\" >0.27</td>\n",
-       "      <td id=\"T_26958_row5_col4\" class=\"data row5 col4\" >0.26</td>\n",
-       "      <td id=\"T_26958_row5_col5\" class=\"data row5 col5\" >0.23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row6\" class=\"row_heading level0 row6\" >mean symmetry</th>\n",
-       "      <td id=\"T_26958_row6_col0\" class=\"data row6 col0\" >0.05</td>\n",
-       "      <td id=\"T_26958_row6_col1\" class=\"data row6 col1\" >-0.01</td>\n",
-       "      <td id=\"T_26958_row6_col2\" class=\"data row6 col2\" >0.06</td>\n",
-       "      <td id=\"T_26958_row6_col3\" class=\"data row6 col3\" >0.22</td>\n",
-       "      <td id=\"T_26958_row6_col4\" class=\"data row6 col4\" >-0.08</td>\n",
-       "      <td id=\"T_26958_row6_col5\" class=\"data row6 col5\" >0.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row7\" class=\"row_heading level0 row7\" >concavity error</th>\n",
-       "      <td id=\"T_26958_row7_col0\" class=\"data row7 col0\" >-0.06</td>\n",
-       "      <td id=\"T_26958_row7_col1\" class=\"data row7 col1\" >-0.16</td>\n",
-       "      <td id=\"T_26958_row7_col2\" class=\"data row7 col2\" >0.06</td>\n",
-       "      <td id=\"T_26958_row7_col3\" class=\"data row7 col3\" >0.10</td>\n",
-       "      <td id=\"T_26958_row7_col4\" class=\"data row7 col4\" >0.25</td>\n",
-       "      <td id=\"T_26958_row7_col5\" class=\"data row7 col5\" >0.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row8\" class=\"row_heading level0 row8\" >worst compactness</th>\n",
-       "      <td id=\"T_26958_row8_col0\" class=\"data row8 col0\" >0.01</td>\n",
-       "      <td id=\"T_26958_row8_col1\" class=\"data row8 col1\" >0.28</td>\n",
-       "      <td id=\"T_26958_row8_col2\" class=\"data row8 col2\" >-0.07</td>\n",
-       "      <td id=\"T_26958_row8_col3\" class=\"data row8 col3\" >-0.07</td>\n",
-       "      <td id=\"T_26958_row8_col4\" class=\"data row8 col4\" >-0.00</td>\n",
-       "      <td id=\"T_26958_row8_col5\" class=\"data row8 col5\" >0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row9\" class=\"row_heading level0 row9\" >mean smoothness</th>\n",
-       "      <td id=\"T_26958_row9_col0\" class=\"data row9 col0\" >-0.23</td>\n",
-       "      <td id=\"T_26958_row9_col1\" class=\"data row9 col1\" >0.04</td>\n",
-       "      <td id=\"T_26958_row9_col2\" class=\"data row9 col2\" >-0.29</td>\n",
-       "      <td id=\"T_26958_row9_col3\" class=\"data row9 col3\" >-0.13</td>\n",
-       "      <td id=\"T_26958_row9_col4\" class=\"data row9 col4\" >-0.24</td>\n",
-       "      <td id=\"T_26958_row9_col5\" class=\"data row9 col5\" >-0.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row10\" class=\"row_heading level0 row10\" >smoothness error</th>\n",
-       "      <td id=\"T_26958_row10_col0\" class=\"data row10 col0\" >-0.08</td>\n",
-       "      <td id=\"T_26958_row10_col1\" class=\"data row10 col1\" >-0.29</td>\n",
-       "      <td id=\"T_26958_row10_col2\" class=\"data row10 col2\" >-0.16</td>\n",
-       "      <td id=\"T_26958_row10_col3\" class=\"data row10 col3\" >-0.21</td>\n",
-       "      <td id=\"T_26958_row10_col4\" class=\"data row10 col4\" >-0.35</td>\n",
-       "      <td id=\"T_26958_row10_col5\" class=\"data row10 col5\" >-0.22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row11\" class=\"row_heading level0 row11\" >concave points error</th>\n",
-       "      <td id=\"T_26958_row11_col0\" class=\"data row11 col0\" >-0.10</td>\n",
-       "      <td id=\"T_26958_row11_col1\" class=\"data row11 col1\" >-0.45</td>\n",
-       "      <td id=\"T_26958_row11_col2\" class=\"data row11 col2\" >-0.25</td>\n",
-       "      <td id=\"T_26958_row11_col3\" class=\"data row11 col3\" >-0.30</td>\n",
-       "      <td id=\"T_26958_row11_col4\" class=\"data row11 col4\" >-0.31</td>\n",
-       "      <td id=\"T_26958_row11_col5\" class=\"data row11 col5\" >-0.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row12\" class=\"row_heading level0 row12\" >mean perimeter</th>\n",
-       "      <td id=\"T_26958_row12_col0\" class=\"data row12 col0\" >-0.64</td>\n",
-       "      <td id=\"T_26958_row12_col1\" class=\"data row12 col1\" >-0.31</td>\n",
-       "      <td id=\"T_26958_row12_col2\" class=\"data row12 col2\" >-0.28</td>\n",
-       "      <td id=\"T_26958_row12_col3\" class=\"data row12 col3\" >-0.30</td>\n",
-       "      <td id=\"T_26958_row12_col4\" class=\"data row12 col4\" >-0.36</td>\n",
-       "      <td id=\"T_26958_row12_col5\" class=\"data row12 col5\" >-0.38</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row13\" class=\"row_heading level0 row13\" >mean radius</th>\n",
-       "      <td id=\"T_26958_row13_col0\" class=\"data row13 col0\" >-0.65</td>\n",
-       "      <td id=\"T_26958_row13_col1\" class=\"data row13 col1\" >-0.30</td>\n",
-       "      <td id=\"T_26958_row13_col2\" class=\"data row13 col2\" >-0.31</td>\n",
-       "      <td id=\"T_26958_row13_col3\" class=\"data row13 col3\" >-0.31</td>\n",
-       "      <td id=\"T_26958_row13_col4\" class=\"data row13 col4\" >-0.37</td>\n",
-       "      <td id=\"T_26958_row13_col5\" class=\"data row13 col5\" >-0.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row14\" class=\"row_heading level0 row14\" >worst fractal dimension</th>\n",
-       "      <td id=\"T_26958_row14_col0\" class=\"data row14 col0\" >-0.56</td>\n",
-       "      <td id=\"T_26958_row14_col1\" class=\"data row14 col1\" >-0.56</td>\n",
-       "      <td id=\"T_26958_row14_col2\" class=\"data row14 col2\" >-0.52</td>\n",
-       "      <td id=\"T_26958_row14_col3\" class=\"data row14 col3\" >-0.35</td>\n",
-       "      <td id=\"T_26958_row14_col4\" class=\"data row14 col4\" >-0.11</td>\n",
-       "      <td id=\"T_26958_row14_col5\" class=\"data row14 col5\" >-0.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row15\" class=\"row_heading level0 row15\" >mean area</th>\n",
-       "      <td id=\"T_26958_row15_col0\" class=\"data row15 col0\" >-0.66</td>\n",
-       "      <td id=\"T_26958_row15_col1\" class=\"data row15 col1\" >-0.43</td>\n",
-       "      <td id=\"T_26958_row15_col2\" class=\"data row15 col2\" >-0.37</td>\n",
-       "      <td id=\"T_26958_row15_col3\" class=\"data row15 col3\" >-0.39</td>\n",
-       "      <td id=\"T_26958_row15_col4\" class=\"data row15 col4\" >-0.38</td>\n",
-       "      <td id=\"T_26958_row15_col5\" class=\"data row15 col5\" >-0.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row16\" class=\"row_heading level0 row16\" >mean texture</th>\n",
-       "      <td id=\"T_26958_row16_col0\" class=\"data row16 col0\" >-0.55</td>\n",
-       "      <td id=\"T_26958_row16_col1\" class=\"data row16 col1\" >-0.37</td>\n",
-       "      <td id=\"T_26958_row16_col2\" class=\"data row16 col2\" >-0.49</td>\n",
-       "      <td id=\"T_26958_row16_col3\" class=\"data row16 col3\" >-0.36</td>\n",
-       "      <td id=\"T_26958_row16_col4\" class=\"data row16 col4\" >-0.50</td>\n",
-       "      <td id=\"T_26958_row16_col5\" class=\"data row16 col5\" >-0.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row17\" class=\"row_heading level0 row17\" >perimeter error</th>\n",
-       "      <td id=\"T_26958_row17_col0\" class=\"data row17 col0\" >-0.84</td>\n",
-       "      <td id=\"T_26958_row17_col1\" class=\"data row17 col1\" >-0.66</td>\n",
-       "      <td id=\"T_26958_row17_col2\" class=\"data row17 col2\" >-0.65</td>\n",
-       "      <td id=\"T_26958_row17_col3\" class=\"data row17 col3\" >-0.42</td>\n",
-       "      <td id=\"T_26958_row17_col4\" class=\"data row17 col4\" >-0.76</td>\n",
-       "      <td id=\"T_26958_row17_col5\" class=\"data row17 col5\" >-0.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row18\" class=\"row_heading level0 row18\" >worst smoothness</th>\n",
-       "      <td id=\"T_26958_row18_col0\" class=\"data row18 col0\" >-1.14</td>\n",
-       "      <td id=\"T_26958_row18_col1\" class=\"data row18 col1\" >-0.56</td>\n",
-       "      <td id=\"T_26958_row18_col2\" class=\"data row18 col2\" >-0.52</td>\n",
-       "      <td id=\"T_26958_row18_col3\" class=\"data row18 col3\" >-0.52</td>\n",
-       "      <td id=\"T_26958_row18_col4\" class=\"data row18 col4\" >-0.66</td>\n",
-       "      <td id=\"T_26958_row18_col5\" class=\"data row18 col5\" >-0.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row19\" class=\"row_heading level0 row19\" >worst perimeter</th>\n",
-       "      <td id=\"T_26958_row19_col0\" class=\"data row19 col0\" >-0.88</td>\n",
-       "      <td id=\"T_26958_row19_col1\" class=\"data row19 col1\" >-0.75</td>\n",
-       "      <td id=\"T_26958_row19_col2\" class=\"data row19 col2\" >-0.81</td>\n",
-       "      <td id=\"T_26958_row19_col3\" class=\"data row19 col3\" >-0.80</td>\n",
-       "      <td id=\"T_26958_row19_col4\" class=\"data row19 col4\" >-0.74</td>\n",
-       "      <td id=\"T_26958_row19_col5\" class=\"data row19 col5\" >-0.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row20\" class=\"row_heading level0 row20\" >mean concavity</th>\n",
-       "      <td id=\"T_26958_row20_col0\" class=\"data row20 col0\" >-0.74</td>\n",
-       "      <td id=\"T_26958_row20_col1\" class=\"data row20 col1\" >-0.92</td>\n",
-       "      <td id=\"T_26958_row20_col2\" class=\"data row20 col2\" >-0.67</td>\n",
-       "      <td id=\"T_26958_row20_col3\" class=\"data row20 col3\" >-0.87</td>\n",
-       "      <td id=\"T_26958_row20_col4\" class=\"data row20 col4\" >-0.78</td>\n",
-       "      <td id=\"T_26958_row20_col5\" class=\"data row20 col5\" >-0.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row21\" class=\"row_heading level0 row21\" >worst symmetry</th>\n",
-       "      <td id=\"T_26958_row21_col0\" class=\"data row21 col0\" >-0.87</td>\n",
-       "      <td id=\"T_26958_row21_col1\" class=\"data row21 col1\" >-0.72</td>\n",
-       "      <td id=\"T_26958_row21_col2\" class=\"data row21 col2\" >-0.73</td>\n",
-       "      <td id=\"T_26958_row21_col3\" class=\"data row21 col3\" >-0.89</td>\n",
-       "      <td id=\"T_26958_row21_col4\" class=\"data row21 col4\" >-0.93</td>\n",
-       "      <td id=\"T_26958_row21_col5\" class=\"data row21 col5\" >-0.83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row22\" class=\"row_heading level0 row22\" >worst concavity</th>\n",
-       "      <td id=\"T_26958_row22_col0\" class=\"data row22 col0\" >-0.84</td>\n",
-       "      <td id=\"T_26958_row22_col1\" class=\"data row22 col1\" >-0.98</td>\n",
-       "      <td id=\"T_26958_row22_col2\" class=\"data row22 col2\" >-0.76</td>\n",
-       "      <td id=\"T_26958_row22_col3\" class=\"data row22 col3\" >-0.49</td>\n",
-       "      <td id=\"T_26958_row22_col4\" class=\"data row22 col4\" >-1.13</td>\n",
-       "      <td id=\"T_26958_row22_col5\" class=\"data row22 col5\" >-0.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row23\" class=\"row_heading level0 row23\" >worst concave points</th>\n",
-       "      <td id=\"T_26958_row23_col0\" class=\"data row23 col0\" >-0.72</td>\n",
-       "      <td id=\"T_26958_row23_col1\" class=\"data row23 col1\" >-0.91</td>\n",
-       "      <td id=\"T_26958_row23_col2\" class=\"data row23 col2\" >-0.82</td>\n",
-       "      <td id=\"T_26958_row23_col3\" class=\"data row23 col3\" >-0.97</td>\n",
-       "      <td id=\"T_26958_row23_col4\" class=\"data row23 col4\" >-0.89</td>\n",
-       "      <td id=\"T_26958_row23_col5\" class=\"data row23 col5\" >-0.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row24\" class=\"row_heading level0 row24\" >mean concave points</th>\n",
-       "      <td id=\"T_26958_row24_col0\" class=\"data row24 col0\" >-0.63</td>\n",
-       "      <td id=\"T_26958_row24_col1\" class=\"data row24 col1\" >-0.84</td>\n",
-       "      <td id=\"T_26958_row24_col2\" class=\"data row24 col2\" >-0.94</td>\n",
-       "      <td id=\"T_26958_row24_col3\" class=\"data row24 col3\" >-1.14</td>\n",
-       "      <td id=\"T_26958_row24_col4\" class=\"data row24 col4\" >-0.86</td>\n",
-       "      <td id=\"T_26958_row24_col5\" class=\"data row24 col5\" >-0.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row25\" class=\"row_heading level0 row25\" >area error</th>\n",
-       "      <td id=\"T_26958_row25_col0\" class=\"data row25 col0\" >-0.97</td>\n",
-       "      <td id=\"T_26958_row25_col1\" class=\"data row25 col1\" >-0.99</td>\n",
-       "      <td id=\"T_26958_row25_col2\" class=\"data row25 col2\" >-0.93</td>\n",
-       "      <td id=\"T_26958_row25_col3\" class=\"data row25 col3\" >-0.79</td>\n",
-       "      <td id=\"T_26958_row25_col4\" class=\"data row25 col4\" >-0.88</td>\n",
-       "      <td id=\"T_26958_row25_col5\" class=\"data row25 col5\" >-0.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row26\" class=\"row_heading level0 row26\" >worst area</th>\n",
-       "      <td id=\"T_26958_row26_col0\" class=\"data row26 col0\" >-0.98</td>\n",
-       "      <td id=\"T_26958_row26_col1\" class=\"data row26 col1\" >-0.95</td>\n",
-       "      <td id=\"T_26958_row26_col2\" class=\"data row26 col2\" >-0.94</td>\n",
-       "      <td id=\"T_26958_row26_col3\" class=\"data row26 col3\" >-0.94</td>\n",
-       "      <td id=\"T_26958_row26_col4\" class=\"data row26 col4\" >-0.85</td>\n",
-       "      <td id=\"T_26958_row26_col5\" class=\"data row26 col5\" >-0.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row27\" class=\"row_heading level0 row27\" >worst radius</th>\n",
-       "      <td id=\"T_26958_row27_col0\" class=\"data row27 col0\" >-1.01</td>\n",
-       "      <td id=\"T_26958_row27_col1\" class=\"data row27 col1\" >-0.93</td>\n",
-       "      <td id=\"T_26958_row27_col2\" class=\"data row27 col2\" >-1.00</td>\n",
-       "      <td id=\"T_26958_row27_col3\" class=\"data row27 col3\" >-1.03</td>\n",
-       "      <td id=\"T_26958_row27_col4\" class=\"data row27 col4\" >-0.86</td>\n",
-       "      <td id=\"T_26958_row27_col5\" class=\"data row27 col5\" >-0.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row28\" class=\"row_heading level0 row28\" >radius error</th>\n",
-       "      <td id=\"T_26958_row28_col0\" class=\"data row28 col0\" >-1.17</td>\n",
-       "      <td id=\"T_26958_row28_col1\" class=\"data row28 col1\" >-1.17</td>\n",
-       "      <td id=\"T_26958_row28_col2\" class=\"data row28 col2\" >-1.27</td>\n",
-       "      <td id=\"T_26958_row28_col3\" class=\"data row28 col3\" >-1.11</td>\n",
-       "      <td id=\"T_26958_row28_col4\" class=\"data row28 col4\" >-1.19</td>\n",
-       "      <td id=\"T_26958_row28_col5\" class=\"data row28 col5\" >-1.18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_26958_level0_row29\" class=\"row_heading level0 row29\" >worst texture</th>\n",
-       "      <td id=\"T_26958_row29_col0\" class=\"data row29 col0\" >-1.10</td>\n",
-       "      <td id=\"T_26958_row29_col1\" class=\"data row29 col1\" >-1.16</td>\n",
-       "      <td id=\"T_26958_row29_col2\" class=\"data row29 col2\" >-1.08</td>\n",
-       "      <td id=\"T_26958_row29_col3\" class=\"data row29 col3\" >-1.31</td>\n",
-       "      <td id=\"T_26958_row29_col4\" class=\"data row29 col4\" >-1.28</td>\n",
-       "      <td id=\"T_26958_row29_col5\" class=\"data row29 col5\" >-1.19</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x14ddf69e0>"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show all feature coefficients across folds:\n",
-    "(\n",
-    "    ds.model.sel(models=\"lr\")\n",
-    "    .to_dataframe()\n",
-    "    .assign(selected_features=lambda df: \n",
-    "        df['model'].apply(lambda m: \n",
-    "            tuple(zip(\n",
-    "                m.estimator[-1].coef_.squeeze(),\n",
-    "                m.estimator[:-1].get_feature_names_out()\n",
-    "            ))\n",
-    "        )\n",
-    "    )\n",
-    "    .explode('selected_features')\n",
-    "    .pipe(lambda df: pd.DataFrame(df['selected_features'].tolist(), index=df.index, columns=['value', 'feature']))\n",
-    "    .reset_index()\n",
-    "    .pivot(index='feature', columns='folds', values='value')\n",
-    "    .assign(mean=lambda df: df.mean(axis=1))\n",
-    "    .sort_values('mean', ascending=False)\n",
-    "    .style\n",
-    "    .format(precision=2)\n",
-    "    .background_gradient(axis=None, cmap='Spectral')\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b6bf164d-c446-4b57-b01a-22b7395f81ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>outcome</th>\n",
-       "      <th>classes</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>index</th>\n",
-       "      <th>models</th>\n",
-       "      <th>outcomes</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R000</th>\n",
-       "      <th>lr</th>\n",
-       "      <th>target</th>\n",
-       "      <td>9.081945e-16</td>\n",
-       "      <td>0</td>\n",
-       "      <td>positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R001</th>\n",
-       "      <th>lr</th>\n",
-       "      <th>target</th>\n",
-       "      <td>1.719733e-08</td>\n",
-       "      <td>0</td>\n",
-       "      <td>positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R002</th>\n",
-       "      <th>lr</th>\n",
-       "      <th>target</th>\n",
-       "      <td>3.225580e-08</td>\n",
-       "      <td>0</td>\n",
-       "      <td>positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R003</th>\n",
-       "      <th>lr</th>\n",
-       "      <th>target</th>\n",
-       "      <td>8.920052e-01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R004</th>\n",
-       "      <th>lr</th>\n",
-       "      <th>target</th>\n",
-       "      <td>2.059340e-05</td>\n",
-       "      <td>0</td>\n",
-       "      <td>positive</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         prediction  outcome   classes\n",
-       "index models outcomes                                 \n",
-       "R000  lr     target    9.081945e-16        0  positive\n",
-       "R001  lr     target    1.719733e-08        0  positive\n",
-       "R002  lr     target    3.225580e-08        0  positive\n",
-       "R003  lr     target    8.920052e-01        0  positive\n",
-       "R004  lr     target    2.059340e-05        0  positive"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show predictions and true outcomes on test splits\n",
-    "ds[['prediction', 'outcome']].sel(classes='positive').to_dataframe().head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "b0eb1757-7d99-4be0-be97-39770e914a38",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_12ce3_row0_col1, #T_12ce3_row0_col3, #T_12ce3_row2_col1, #T_12ce3_row2_col3 {\n",
-       "  background-color: #fff7fb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_12ce3_row0_col2 {\n",
-       "  background-color: #f6eff7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_12ce3_row0_col4, #T_12ce3_row1_col3, #T_12ce3_row1_col4, #T_12ce3_row2_col4, #T_12ce3_row3_col3, #T_12ce3_row3_col4 {\n",
-       "  background-color: #023858;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_12ce3_row1_col1 {\n",
-       "  background-color: #f9f2f8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_12ce3_row1_col2 {\n",
-       "  background-color: #023f64;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_12ce3_row2_col2 {\n",
-       "  background-color: #f5eff6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_12ce3_row3_col1 {\n",
-       "  background-color: #bdc8e1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_12ce3_row3_col2 {\n",
-       "  background-color: #034165;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_12ce3\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank\" >&nbsp;</th>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_12ce3_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
-       "      <th id=\"T_12ce3_level0_col1\" class=\"col_heading level0 col1\" >min</th>\n",
-       "      <th id=\"T_12ce3_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
-       "      <th id=\"T_12ce3_level0_col3\" class=\"col_heading level0 col3\" >50%</th>\n",
-       "      <th id=\"T_12ce3_level0_col4\" class=\"col_heading level0 col4\" >max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >models</th>\n",
-       "      <th class=\"index_name level1\" >outcome</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12ce3_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">gbm</th>\n",
-       "      <th id=\"T_12ce3_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
-       "      <td id=\"T_12ce3_row0_col0\" class=\"data row0 col0\" >212</td>\n",
-       "      <td id=\"T_12ce3_row0_col1\" class=\"data row0 col1\" >0.000</td>\n",
-       "      <td id=\"T_12ce3_row0_col2\" class=\"data row0 col2\" >0.061</td>\n",
-       "      <td id=\"T_12ce3_row0_col3\" class=\"data row0 col3\" >0.001</td>\n",
-       "      <td id=\"T_12ce3_row0_col4\" class=\"data row0 col4\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12ce3_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
-       "      <td id=\"T_12ce3_row1_col0\" class=\"data row1 col0\" >357</td>\n",
-       "      <td id=\"T_12ce3_row1_col1\" class=\"data row1 col1\" >0.042</td>\n",
-       "      <td id=\"T_12ce3_row1_col2\" class=\"data row1 col2\" >0.970</td>\n",
-       "      <td id=\"T_12ce3_row1_col3\" class=\"data row1 col3\" >0.999</td>\n",
-       "      <td id=\"T_12ce3_row1_col4\" class=\"data row1 col4\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12ce3_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"2\">lr</th>\n",
-       "      <th id=\"T_12ce3_level1_row2\" class=\"row_heading level1 row2\" >0</th>\n",
-       "      <td id=\"T_12ce3_row2_col0\" class=\"data row2 col0\" >212</td>\n",
-       "      <td id=\"T_12ce3_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_12ce3_row2_col2\" class=\"data row2 col2\" >0.064</td>\n",
-       "      <td id=\"T_12ce3_row2_col3\" class=\"data row2 col3\" >0.000</td>\n",
-       "      <td id=\"T_12ce3_row2_col4\" class=\"data row2 col4\" >0.998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12ce3_level1_row3\" class=\"row_heading level1 row3\" >1</th>\n",
-       "      <td id=\"T_12ce3_row3_col0\" class=\"data row3 col0\" >357</td>\n",
-       "      <td id=\"T_12ce3_row3_col1\" class=\"data row3 col1\" >0.306</td>\n",
-       "      <td id=\"T_12ce3_row3_col2\" class=\"data row3 col2\" >0.965</td>\n",
-       "      <td id=\"T_12ce3_row3_col3\" class=\"data row3 col3\" >0.998</td>\n",
-       "      <td id=\"T_12ce3_row3_col4\" class=\"data row3 col4\" >1.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x14183d000>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show test prediction distributions by true label\n",
-    "(\n",
-    "    ds[['prediction', 'outcome']]\n",
-    "    .sel(classes='positive')\n",
-    "    .to_dataframe()\n",
-    "    .reset_index()\n",
-    "    .groupby(['models', 'outcome'])['prediction'].describe()\n",
-    "    [['count', 'min', 'mean', '50%', 'max']]\n",
-    "    .style\n",
-    "    .format(precision=3)\n",
-    "    .background_gradient(axis=None, subset=['min', 'mean', '50%', 'max'])\n",
-    "    # .background_gradient(subset='count')\n",
-    "    .format(precision=0, subset='count')\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index b8e2525..b441958 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,12 +37,6 @@ requires = ["setuptools >= 45", "setuptools_scm[toml] >= 6.2"]
 
 [tool.setuptools_scm]
 
-[options]
-package_dir=xrml
-
-[options.packages.find]
-where=xrml
-
 [tool.ruff]
 target-version = "py310"
 line-length = 88