doc/notebook/examples.ipynb

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   "source": [
    "### Basic `xrml` examples"
   ]
  },
  {
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   "id": "702165a5-33c6-4a53-bf1b-56bc09d95ecc",
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   "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"
   ]
  },
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   "execution_count": 2,
   "id": "8b131c9b-24e0-4fe8-b079-0f1c3d8254bf",
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      "<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"
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       "  <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",
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       "    <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",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "      <td>R004</td>\n",
       "      <td>breast_cancer.csv</td>\n",
       "      <td>.. _breast_cancer_dataset:\\n\\nBreast cancer wi...</td>\n",
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       "<p>5 rows × 34 columns</p>\n",
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       "   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]"
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     "execution_count": 2,
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   "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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     "name": "stderr",
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     "text": [
      "100%|██████████| 5/5 [00:02<00:00,  1.74it/s]\n"
     ]
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       ".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": [
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       "  background-color: #04639b;\n",
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       "}\n",
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b08df_row8_col1 {\n",
       "  background-color: #046097;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #056faf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "}\n",
       "</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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       "  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",
    ")"
   ]
  }
 ],
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