Rupa_chapter_3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/Rupa_chapter_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZevXooK9AlL5"
      },
      "source": [
        "#### In this chapter we will be using the MNIST dataset, which is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “hello world” of machine learning: whenever people come up with a new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns machine learning tackles this dataset sooner or later."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKJm7Cq0BB7x"
      },
      "source": [
        "# 1. Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKIRh8H9BIBG"
      },
      "source": [
        "#### This project requires Python 3.7 or above:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uqgr14PPAaVb"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Id5ucrniBPZE"
      },
      "outputs": [],
      "source": [
        "# It also requires Scikit-Learn ≥ 1.0.1:\n",
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vGdFTBvVBbIJ"
      },
      "outputs": [],
      "source": [
        "# let's define the default font sizes to make the figures prettier:\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=14)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=14)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTqwExNGE6-1"
      },
      "source": [
        "# 2. MNIST"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwX_klEaE-P8"
      },
      "source": [
        "#### The sklearn.datasets package contains mostly three types of functions: fetch_* functions such as fetch_openml() to download real-life datasets, load_* functions to load small toy datasets bundled with Scikit-Learn (so they don’t need to be down‐ loaded over the internet), and make_* functions to generate fake datasets, useful for tests. Generated datasets are usually returned as an (X, y) tuple containing the input data and the targets, both as NumPy arrays. Other datasets are returned as sklearn.utils.Bunch objects, which are dictionaries whose entries can also be accessed as attributes. They generally contain the following entries:\n",
        "\"DESCR\"\n",
        "A description of the dataset\n",
        "\"data\"\n",
        "The input data, usually as a 2D NumPy array\n",
        "\"target\"\n",
        "The labels, usually as a 1D NumPy array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4s40LCRGCu3G",
        "outputId": "65ea6355-9d77-4780-96fd-f107c17718cf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "# here we will use fetch_openml()\n",
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "mnist = fetch_openml('mnist_784', as_frame=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTqZnrVyIgXQ",
        "outputId": "160b38cd-c4b1-4e97-cfaf-d25b5e479f44"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \n",
            "**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \n",
            "**Please cite**:  \n",
            "\n",
            "The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \n",
            "\n",
            "It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \n",
            "\n",
            "With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \n",
            "\n",
            "The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\n",
            "\n",
            "Downloaded from openml.org.\n"
          ]
        }
      ],
      "source": [
        "# extra code – it's a bit too long\n",
        "print(mnist.DESCR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m154r_UEJC82",
        "outputId": "63ffebdf-5513-4e6f-fb26-73914d0ada6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# gives a list of all keys\n",
        "mnist.keys()  # extra code – we only use data and target in this notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrHCZQJyJTS7",
        "outputId": "e7729bae-2a76-41fc-e229-a7f8e0ab9c8b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X, y = mnist.data, mnist.target\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZ72R69eJjGa",
        "outputId": "bc160053-2f11-4305-b911-2a44530b8f5d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qaL7oJuJnQS",
        "outputId": "8acdf0b1-4888-4c79-cbc7-235d7c474948"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['5', '0', '4', ..., '4', '5', '6'], dtype=object)"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7hd9zR3Jpu4",
        "outputId": "c3bb385e-06c7-4027-eada-50e2d5c9d24a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Fmj4kb2J_y6"
      },
      "source": [
        "#### There are 70,000 images, and each image has 784 features. This is because each image is 28 × 28 pixels, and each feature simply represents one pixel’s intensity, from 0 (white) to 255 (black). Let’s take a peek at one digit from the dataset (Figure 3-1). All we need to do is grab an instance’s feature vector, reshape it to a 28 × 28 array, and display it using Matplotlib’s imshow() function. We use cmap=\"binary\" to get a grayscale color map where 0 is white and 255 is black:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "Tv1Yan4XJ5E7",
        "outputId": "209948c7-30b7-4078-84a5-ebd72e7c9f2a"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_digit(image_data):\n",
        "    image = image_data.reshape(28, 28)\n",
        "    plt.imshow(image, cmap=\"binary\")\n",
        "    plt.axis(\"off\")\n",
        "\n",
        "some_digit = X[2]\n",
        "plot_digit(some_digit)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "DRAiFtATLlvX",
        "outputId": "011ab282-23c1-4efa-f002-e4d3b611284e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'4'"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking what label says for the image we got\n",
        "y[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMSVpj_sMJzu"
      },
      "source": [
        "####  You should always create a test set and set it aside before inspecting the data closely. The MNIST dataset returned by fetch_openml() is actually already split into a training set (the first 60,000 images) and a test set (the last 10,000 images).The training set is already shuffled for us, which is good because this guarantees that all cross-validation folds will be similar (we don’t want one fold to be missing some digits). Moreover, some learning algorithms are sensitive to the order of the training instances, and they perform poorly if they get many similar instances in a row. Shuffling the dataset ensures that this won’t happen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dOip4yrcMBDK"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "rfebS1W9Opu6",
        "outputId": "54a24b3d-5cce-4ca9-adee-2cd314d033f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 100 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# extra code – this cell generates Figure 3–2\n",
        "plt.figure(figsize=(10, 10))\n",
        "for x, image_data in enumerate(X[:100]):\n",
        "    plt.subplot(10, 10, x + 1)\n",
        "    plt.axis(\"on\")\n",
        "    plot_digit(image_data)\n",
        "plt.subplots_adjust(wspace=2, hspace=2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qg_QLzRPeRDQ"
      },
      "source": [
        "# Training a Binary Classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Z807w7OeSpp"
      },
      "outputs": [],
      "source": [
        "y_train_4 = (y_train == '4')  # True for all 4s, False for all other digits\n",
        "y_test_4 = (y_test == '4')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJWrukTue-gR"
      },
      "source": [
        "A good place to start is with a stochastic gra‐ dient descent (SGD, or stochastic GD) classifier, using Scikit-Learn’s SGDClassifier class. This classifier is capable of handling very large datasets efficiently. This is in part because SGD deals with training instances independently, one at a time, which also makes SGD well suited for online learning, as you will see later. Let’s create an SGDClassifier and train it on the whole training set:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "gKd4DWRYeuhl",
        "outputId": "2ed4be63-1832-4fda-ba07-e046cdf0fb25"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier(random_state=42)\n",
        "sgd_clf.fit(X_train, y_train_4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApBUzE9vfbc5",
        "outputId": "b4ee53be-68d3-4818-f19b-a336cb40ae82"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2zifchLnr2N"
      },
      "source": [
        "# Performance Measures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOX_8RzVnv8Z"
      },
      "source": [
        "## Measuring Accuracy Using Cross-Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRnmQoiBnxVP",
        "outputId": "5ebfaa08-6aab-43ea-96a3-b427cbd22c5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.9671 , 0.97645, 0.9687 ])"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val_score(sgd_clf, X_train, y_train_4, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-WQZrMhmeWu",
        "outputId": "8083fb1b-993b-4ed0-fe7c-b744f0cb8e08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9671\n",
            "0.97645\n",
            "0.9687\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.base import clone\n",
        "\n",
        "skfolds = StratifiedKFold(n_splits=3)  # add shuffle=True if the dataset is not\n",
        "                                       # already shuffled\n",
        "for train_index, test_index in skfolds.split(X_train, y_train_4):\n",
        "    clone_clf = clone(sgd_clf)\n",
        "    X_train_folds = X_train[train_index]\n",
        "    y_train_folds = y_train_4[train_index]\n",
        "    X_test_fold = X_train[test_index]\n",
        "    y_test_fold = y_train_4[test_index]\n",
        "\n",
        "    clone_clf.fit(X_train_folds, y_train_folds)\n",
        "    y_pred = clone_clf.predict(X_test_fold)\n",
        "    n_correct = sum(y_pred == y_test_fold)\n",
        "    print(n_correct / len(y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p55FVxQpqbyj",
        "outputId": "a1c8a7c9-df2c-4858-975e-f763be319375"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False\n"
          ]
        }
      ],
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "dummy_clf = DummyClassifier()\n",
        "dummy_clf.fit(X_train, y_train_4)\n",
        "print(any(dummy_clf.predict(X_train))) # prints False : no 4s detected"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHQEmDnFqqle",
        "outputId": "7fc6eb1c-3d08-49c1-90b8-1cf90c3db6fc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.90265, 0.90265, 0.9026 ])"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cross_val_score(dummy_clf, X_train, y_train_4, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asNxA0yzrZJH"
      },
      "source": [
        "### accuracy is not the best measure for classifiers, especially when dealing with skewed datasets , A much better way to evaluate the performance of a clasiifier is to look at the confusion matrix (CM)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Vy2VyDhsXeC"
      },
      "source": [
        "### Confusion Matrices\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cor_gIn2u1Os"
      },
      "source": [
        "To compute the confusion matrix, you first need to have a set of predictions so that they can be compared to the actual targets. You could make predictions on the test set, but it’s best to keep that untouched for now (remember that you want to use the test set only at the very end of your project, once you have a classifier that you are ready to launch). Instead, you can use the cross_val_predict() function:\n",
        "\n",
        "Just like the cross_val_score() function, cross_val_predict() performs k-fold cross-validation, but instead of returning the evaluation scores, it returns the predic‐ tions made on each test fold. This means that you get a clean prediction for each instance in the training set (by “clean” I mean “out-of-sample”: the model makes predictions on data that it never saw during training)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p1ORJ2RUu5pj"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_4, cv=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zSWAF-f2xxcs",
        "outputId": "fa7f2bfb-15fe-4447-8687-0b661d88462b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[52957,  1201],\n",
              "       [  554,  5288]])"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_train_4, y_train_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MonJnng2yDRU"
      },
      "source": [
        "Each row in a confusion matrix represents an actual class, while each column rep‐ resents a predicted class. The first row of this matrix considers non-5 images (the negative class): 53,892 of them were correctly classified as non-5s (they are called true negatives), while the remaining 687 were wrongly classified as 5s (false positives, also called type I errors). The second row considers the images of 5s (the positive class): 1,891 were wrongly classified as non-5s (false negatives, also called type II errors), while the remaining 3,530 were correctly classified as 5s (true positives). A perfect classifier would only have true positives and true negatives, so its confusion matrix would have nonzero values only on its main diagonal (top left to bottom right):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njfj5iMxx7TZ",
        "outputId": "8761d032-4670-447f-d055-8f22c597346c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[54158,     0],\n",
              "       [    0,  5842]])"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_perfect_predictions = y_train_4  # pretend we reached perfection\n",
        "confusion_matrix(y_train_4, y_train_perfect_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCn0S9CXyXmi"
      },
      "source": [
        "The confusion matrix gives you a lot of information, but sometimes you may prefer a more concise metric. An interesting one to look at is the accuracy of the positive predictions; this is called the precision of the classifier\n",
        "precision = TP /(TP + FP)\n",
        "precision is typically used along with another metric named recall, also called sensitivity or the true positive rate (TPR): this is the ratio of positive instances that are correctly detected by the classifier\n",
        "recall = TP /(TP + FN)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igO0Xg5jy6Pd"
      },
      "source": [
        "## Precision and Recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFT4B6Udy79Z",
        "outputId": "076a6177-110d-4415-c919-4abef700835d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8149175527816305"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "precision_score(y_train_4, y_train_pred)  # == 5288 / (1201 + 5288)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0QocqXBzS8W",
        "outputId": "afe538ca-bd24-4c9a-c5c9-7140bf8555ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8149175527816305"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# extra code – this cell also computes the precision: TP / (FP + TP)\n",
        "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLG7tX0Zzm_c",
        "outputId": "9e4cf18d-09f2-4028-92c3-2ab45bfbe8ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.905169462512838"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_score(y_train_4, y_train_pred)  # == 5288 / (554 + 5288)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tR3NN1NJ0UKK"
      },
      "source": [
        "Now our 4-detector does not look as shiny as it did when we looked at its accuracy. When it claims an image represents a 4, it is correct only 81.4% of the time. More‐ over, it only detects 65.1% of the 5s. # in the text book for 5's when recall is caluclated for identifying 4's , it detects 90.5% of the 4's."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fW_sE3uD04lE"
      },
      "source": [
        "It is often convenient to combine precision and recall into a single metric called the F1 score, especially when you need a single metric to compare two classifiers. The F1 score is the harmonic mean of precision and recall. Whereas the regular mean treats all values equally, the harmonic mean gives much more weight to low values. As a result, the classifier will only get a high F1 score if both recall and precision are high.\n",
        "\n",
        "Formula for F1 score:\n",
        "\n",
        "=2× precision×recall / (precision+recall)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gL3ZDYEbz2Dw",
        "outputId": "9a83b097-2623-4a99-d02a-d7ca54ae3f72"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8576757764982564"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "f1_score(y_train_4, y_train_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nc84O1Vt7IvK"
      },
      "source": [
        "The F1 score favors classifiers that have similar precision and recall. This is not always what you want: in some contexts you mostly care about precision, and in other contexts you really care about recall. For example, if you trained a classifier to detect videos that are safe for kids, you would probably prefer a classifier that rejects many good videos (low recall) but keeps only safe ones (high precision), rather than a classifier that has a much higher recall but lets a few really bad videos show up in your product (in such cases, you may even want to add a human pipeline to check the classifier’s video selection). On the other hand, suppose you train a classifier to detect shoplifters in surveillance images: it is probably fine if your classifier only has 30% precision as long as it has 99% recall (sure, the security guards will get a few false alerts, but almost all shoplifters will get caught).\n",
        "Unfortunately, you can’t have it both ways: increasing precision reduces recall, and vice versa. This is called the precision/recall trade-off.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbMcOhOXKpXY"
      },
      "source": [
        "## Precision/Recall Trade-off"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvsiSlYT1cZe",
        "outputId": "7cb0add9-e553-4b6e-a5a6-162c3a297c0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([3214.66574913])"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scores = sgd_clf.decision_function([some_digit])\n",
        "y_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tUe1_AfILt1L"
      },
      "outputs": [],
      "source": [
        "threshold = 0\n",
        "y_some_digit_pred = (y_scores > threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LF4D5r5UMTa_",
        "outputId": "b575afba-d398-4fb1-f62c-d2dd9f62a902"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYK8KljzMWq9",
        "outputId": "c1855f4e-d0e2-4b52-dc1a-0d5b71b52398"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# extra code – just shows that y_scores > 0 produces the same result as\n",
        "#              calling predict()\n",
        "y_scores > 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJxT5prMMeOC",
        "outputId": "f96442ef-158e-460e-88b8-79f70ea6dc54"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "threshold = 3000\n",
        "y_some_digit_pred = (y_scores > threshold)\n",
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t3Lvu9DTM87s"
      },
      "outputs": [],
      "source": [
        "y_scores = cross_val_predict(sgd_clf, X_train, y_train_4, cv=3,\n",
        "                             method=\"decision_function\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zzGVJ0enNjF2"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train_4, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "yg2EQ8dbN1zX",
        "outputId": "bfc3e84e-2d4c-4f52-8d64-2b74b0256f55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))  # extra code – it's not needed, just formatting\n",
        "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
        "plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
        "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
        "\n",
        "# extra code – this section just beautifies and saves Figure 3–5\n",
        "idx = (thresholds >= threshold).argmax()  # first index ≥ threshold\n",
        "plt.plot(thresholds[idx], precisions[idx], \"bo\")\n",
        "plt.plot(thresholds[idx], recalls[idx], \"go\")\n",
        "plt.axis([-50000, 50000, 0, 1])\n",
        "plt.grid()\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.legend(loc=\"center right\")\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Uc2hYPDmQARo",
        "outputId": "486354ab-b511-4203-b43e-10af6c3e685c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.patches as patches  # extra code – for the curved arrow\n",
        "\n",
        "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
        "\n",
        "plt.plot(recalls, precisions, linewidth=2, label=\"Precision/Recall curve\")\n",
        "\n",
        "# extra code – just beautifies  Figure\n",
        "plt.plot([recalls[idx], recalls[idx]], [0., precisions[idx]], \"k:\")\n",
        "plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], \"k:\")\n",
        "plt.plot([recalls[idx]], [precisions[idx]], \"ko\",\n",
        "         label=\"Point at threshold 3,000\")\n",
        "plt.gca().add_patch(patches.FancyArrowPatch(\n",
        "    (0.79, 0.60), (0.61, 0.78),\n",
        "    connectionstyle=\"arc3,rad=.2\",\n",
        "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
        "    color=\"#444444\"))\n",
        "plt.text(0.56, 0.62, \"Higher\\nthreshold\", color=\"#333333\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc=\"lower left\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3A4dGipQ2zm",
        "outputId": "5c6055f8-03ce-4468-f9a9-22ea3437b4a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3196.992969466491"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "idx_for_90_precision = (precisions >= 0.90).argmax()\n",
        "threshold_for_90_precision = thresholds[idx_for_90_precision]\n",
        "threshold_for_90_precision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-iU723MgRWyf"
      },
      "outputs": [],
      "source": [
        "y_train_pred_90 = (y_scores >= threshold_for_90_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZfnnsxNRa25",
        "outputId": "45ab6dfc-209d-4082-e4c2-e93854022fbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9001287474710318"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "precision_score(y_train_4, y_train_pred_90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StGZoZuGRety",
        "outputId": "c308ff51-bc66-4e5b-81ca-edd57e5d8355"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8377268058883944"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_at_90_precision = recall_score(y_train_4, y_train_pred_90)\n",
        "recall_at_90_precision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRMXsJKfRnym"
      },
      "source": [
        "## The ROC Curve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXjT05R7FahY"
      },
      "source": [
        "The receiver operating characteristic (ROC) curve is another common tool used with binary classifiers. It is very similar to the precision/recall curve, but instead of plot‐ ting precision versus recall, the ROC curve plots the true positive rate (another name for recall) against the false positive rate (FPR). The FPR (also called the fall-out) is the ratio of negative instances that are incorrectly classified as positive. It is equal to 1 – the true negative rate (TNR), which is the ratio of negative instances that are correctly classified as negative. The TNR is also called specificity. Hence, the ROC curve plots sensitivity (recall) versus 1 – specificity.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TCi1NXTDRo6_"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train_4, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "LWrgaNsKIPLS",
        "outputId": "cf09751b-470e-45f4-f4d9-c0d2d93c2723"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "idx_for_threshold_at_90 = (thresholds <= threshold_for_90_precision).argmax()\n",
        "tpr_90, fpr_90 = tpr[idx_for_threshold_at_90], fpr[idx_for_threshold_at_90]\n",
        "\n",
        "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
        "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
        "plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier's ROC curve\")\n",
        "plt.plot([fpr_90], [tpr_90], \"ko\", label=\"Threshold for 90% precision\")\n",
        "\n",
        "# extra code – just beautifies and saves Figure 3–7\n",
        "plt.gca().add_patch(patches.FancyArrowPatch(\n",
        "    (0.20, 0.89), (0.07, 0.70),\n",
        "    connectionstyle=\"arc3,rad=.4\",\n",
        "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
        "    color=\"#444444\"))\n",
        "plt.text(0.12, 0.71, \"Higher\\nthreshold\", color=\"#333333\")\n",
        "plt.xlabel('False Positive Rate (Fall-Out)')\n",
        "plt.ylabel('True Positive Rate (Recall)')\n",
        "plt.grid()\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.legend(loc=\"lower right\", fontsize=13)\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9bMZH2NI_N4"
      },
      "source": [
        "A ROC curve plotting the false positive rate against the true positive rate for all possible thresholds; the black circle highlights the chosen ratio (at 90% precision and 48% recall)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pyj09n-sJA76"
      },
      "source": [
        "Once again there is a trade-off: the higher the recall (TPR), the more false positives (FPR) the classifier produces. The dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from that line as possible (toward the top-left corner)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsekEEdvJGgX"
      },
      "source": [
        "One way to compare classifiers is to measure the area under the curve (AUC). A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.5. Scikit-Learn provides a function to estimate the ROC AUC:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi_aGpC1Ib6D",
        "outputId": "6a4ce804-ee37-414f-9950-d900de48de72"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9859276923382874"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(y_train_4, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PGucGQZbOZh-"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest_clf = RandomForestClassifier(random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BHw3MNa6Ptr9"
      },
      "outputs": [],
      "source": [
        "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_4, cv=3,\n",
        "                                    method=\"predict_proba\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNB-NOEbROzq",
        "outputId": "521f087b-b414-47a7-f7b0-f2fab0c04b92"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [1., 0.]])"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_probas_forest[:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKuLwELMTrQm"
      },
      "source": [
        "These are estimated probabilities. Among the images that the model classified as positive with a probability between 50% and 60%, there are actually about 94% positive images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCoqg3qvTZYz",
        "outputId": "e316da7d-23e1-4c92-f3f5-2dcdac9a3205"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "94.4%\n"
          ]
        }
      ],
      "source": [
        "# Not in the code\n",
        "idx_50_to_60 = (y_probas_forest[:, 1] > 0.50) & (y_probas_forest[:, 1] < 0.60)\n",
        "print(f\"{(y_train_4[idx_50_to_60]).sum() / idx_50_to_60.sum():.1%}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YyF7HJk9UGxi"
      },
      "outputs": [],
      "source": [
        "y_scores_forest = y_probas_forest[:, 1]\n",
        "precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
        "    y_train_4, y_scores_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Qq_Ym2TmUJlN",
        "outputId": "dc0b0f1d-a249-44b1-8c69-3a1b607dd3f6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
        "\n",
        "plt.plot(recalls_forest, precisions_forest, \"b-\", linewidth=2,\n",
        "         label=\"Random Forest\")\n",
        "plt.plot(recalls, precisions, \"--\", linewidth=2, label=\"SGD\")\n",
        "\n",
        "# extra code – just beautifies and saves Figure 3–8\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc=\"lower left\")\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4Qh_mCyUWjb"
      },
      "source": [
        "Comparing PR curves: the random forest classifier is superior to the SGD classifier because its PR curve is much closer to the top-right corner, and it has a greater AUC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpdNoZfLUv1b"
      },
      "source": [
        "We could use cross_val_predict(forest_clf, X_train, y_train_5, cv=3) to compute y_train_pred_forest, but since we already have the estimated probabilities, we can just use the default threshold of 50% probability to get the same predictions much faster:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEh5D7wEUba9",
        "outputId": "fa1337a2-f2b2-4b36-a093-d6aa51501c56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9347452258122907"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5  # positive proba ≥ 50%\n",
        "f1_score(y_train_4, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZlAUOWUUU2ld",
        "outputId": "aa2a6d3c-3065-48c6-c347-cd2b78ae5078"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9982278527638186"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "roc_auc_score(y_train_4, y_scores_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kz7s1x8BU_uq",
        "outputId": "235fbbad-83c5-45d4-cf4a-c33f17b871ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9917418859227962"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "precision_score(y_train_4, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1cJcLrYVF03",
        "outputId": "e51ecce3-2177-49eb-860f-2846293cef12"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8839438548442314"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_score(y_train_4, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Urt6krVVMHG"
      },
      "source": [
        "Try measuring the precision and recall scores: you should find about 99.1% precision and 88.3% recall. Not too bad!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vo6aog0u2ADj"
      },
      "source": [
        "## Multiclass Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1OiIiL12tsy"
      },
      "source": [
        "Whereas binary classifiers distinguish between two classes, multiclass classifiers (also called multinomial classifiers) can distinguish between more than two classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qT0b5yK9SNvr"
      },
      "source": [
        "Some Scikit-Learn classifiers (e.g., LogisticRegression, RandomForestClassifier, and GaussianNB) are capable of handling multiple classes natively. Others are strictly binary classifiers (e.g., SGDClassifier and SVC). However, there are various strategies that you can use to perform multiclass classification with multiple binary classifiers.\n",
        "One way to create a system that can classify the digit images into 10 classes (from 0 to 9) is to train 10 binary classifiers, one for each digit (a 0-detector, a 1-detector, a 2-detector, and so on). Then when you want to classify an image, you get the decision score from each classifier for that image and you select the class whose classifier outputs the highest score. This is called the one-versus-the-rest (OvR) strategy, or sometimes one-versus-all (OvA).\n",
        "Another strategy is to train a binary classifier for every pair of digits: one to distin‐ guish 0s and 1s, another to distinguish 0s and 2s, another for 1s and 2s, and so on. This is called the one-versus-one (OvO) strategy. If there are N classes, you need to train N × (N – 1) / 2 classifiers. For the MNIST problem, this means training 45 binary classifiers! When you want to classify an image, you have to run the image through all 45 classifiers and see which class wins the most duels. The main advantage of OvO is that each classifier only needs to be trained on the part of the training set containing the two classes that it must distinguish."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXMydl6MSdrW"
      },
      "source": [
        "Some algorithms (such as support vector machine classifiers) scale poorly with the size of the training set. For these algorithms OvO is preferred because it is faster to train many classifiers on small training sets than to train few classifiers on large training sets. For most binary classification algorithms, however, OvR is preferred."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqtwCCDg_wFL"
      },
      "source": [
        "SVMs do not scale well to large datasets, so let's only train on the first 2,000 instances, or else this section will take a very long time to run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Sa4qxBZoVUKR",
        "outputId": "6eb9b89e-81f6-42c9-b0e1-56900e9732b3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVC(random_state=42)"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_clf = SVC(random_state=42)\n",
        "svm_clf.fit(X_train[:2000], y_train[:2000])  # y_train, not y_train_5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3c8Hpl9TFu7"
      },
      "source": [
        "That was easy! We trained the SVC using the original target classes from 0 to 9 (y_train), instead of the 5-versus-the-rest target classes (y_train_5). Since there are 10 classes (i.e., more than 2), Scikit-Learn used the OvO strategy and trained 45 binary classifiers. Now let’s make a prediction on an image:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3SPpFp4_5hF",
        "outputId": "eb52c2ca-3349-40ed-c62c-059fe554156f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['4'], dtype=object)"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGWg2UbfU0ss",
        "outputId": "3a73788b-58da-45d6-8a0f-66305182ea82"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 1.76, -0.29,  7.23,  1.89,  9.3 ,  4.94,  3.84,  6.1 ,  1.75,\n",
              "         8.19]])"
            ]
          },
          "execution_count": 140,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "some_digit_scores = svm_clf.decision_function([some_digit])\n",
        "some_digit_scores.round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjE0vK4yU-35",
        "outputId": "1fc79901-15a4-4585-b2df-be7b873bf45b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_id = some_digit_scores.argmax()\n",
        "class_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxL3GacJVAlx",
        "outputId": "58676e79-1b8a-4843-f6f1-a997ee3d0633"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.classes_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "O5jjCMHvVO8L",
        "outputId": "f212fd9b-ff78-4856-9cb6-d4b8f8fd6a21"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'4'"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.classes_[class_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_bjO3yGVtTG"
      },
      "source": [
        "If you want decision_function() to return all 45 scores, you can set the decision_function_shape hyperparameter to \"ovo\". The default value is \"ovr\", but don't let this confuse you: SVC always uses OvO for training. This hyperparameter only affects whether or not the 45 scores get aggregated or not:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSt8UtomVTSW",
        "outputId": "40199706-90aa-4a81-86d4-fe95e2b5417c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.38, -0.65,  0.  , -1.08, -0.17, -0.13, -0.56, -0.02, -0.46,\n",
              "        -0.9 , -0.84, -1.  , -0.85, -0.77, -0.47, -0.54, -0.69,  0.32,\n",
              "        -1.  ,  0.35,  0.5 ,  0.04,  0.46, -0.08, -1.  , -0.11, -0.1 ,\n",
              "        -0.16,  0.52, -0.16,  1.09,  1.04,  1.05,  1.03,  1.41,  0.13,\n",
              "        -0.27,  0.46, -0.25, -0.18,  0.25, -0.29,  0.21, -0.32, -0.55]])"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# extra code – shows how to get all 45 OvO scores if needed\n",
        "svm_clf.decision_function_shape = \"ovo\"\n",
        "some_digit_scores_ovo = svm_clf.decision_function([some_digit])\n",
        "some_digit_scores_ovo.round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "Z-F0UHIrdTWg",
        "outputId": "45e306ca-833b-40e7-bd76-d4b54e6e757c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=SVC(random_state=42))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsRestClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsRestClassifier(estimator=SVC(random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "OneVsRestClassifier(estimator=SVC(random_state=42))"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "\n",
        "ovr_clf = OneVsRestClassifier(SVC(random_state=42))\n",
        "ovr_clf.fit(X_train[:2000], y_train[:2000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDblT-iydZUT",
        "outputId": "9cd77477-eda6-4a19-e45e-0129d4dad760"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['4'], dtype='<U1')"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ovr_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0tfV31VdaWV",
        "outputId": "3a4bc0ad-63bd-4730-d478-2febd2b42d97"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "execution_count": 147,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(ovr_clf.estimators_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "totric7_fz9_",
        "outputId": "e97f67ea-e51b-47ab-9756-3f44dbe015e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['4'], dtype='<U1')"
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf = SGDClassifier(random_state=42)\n",
        "sgd_clf.fit(X_train, y_train)\n",
        "sgd_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6Gd8AYZg1pj",
        "outputId": "86e0eabe-f09f-4f47-c300-d1dc88a05026"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-34143., -21942.,  -4018.,  -2239.,     43., -15059., -33653.,\n",
              "         -8278.,  -7461., -14180.]])"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.decision_function([some_digit]).round()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAE30dqGhCst",
        "outputId": "1f92b029-a64c-45a3-fca3-22f5ece000ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.87365, 0.85835, 0.8689 ])"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "EG-lgDXzqbc7",
        "outputId": "773e4301-68ab-4b41-9859-7d248a90d988"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.8983, 0.891 , 0.9018])"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train.astype(\"float64\"))\n",
        "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7izvPrlGuwl4"
      },
      "source": [
        "## Error Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "MiEj29JPuv2p",
        "outputId": "efe88078-0975-447b-843a-8da54718a701"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
        "plt.rc('font', size=9)  # extra code – make the text smaller\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "BhRDU06s19t2",
        "outputId": "2f68e450-3f3b-4f5f-f0b1-f36cbfa3ff62"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.rc('font', size=10)  # extra code\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
        "                                        normalize=\"true\", values_format=\".0%\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "mEoO3ocS2PTQ",
        "outputId": "100311a6-5a32-415b-950f-3b8cdfe1bb34"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sample_weight = (y_train_pred != y_train)\n",
        "plt.rc('font', size=10)  # extra code\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
        "                                        sample_weight=sample_weight,\n",
        "                                        normalize=\"true\", values_format=\".0%\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "T0_ROylo2kDS",
        "outputId": "0de8b6eb-74d0-40de-d6f1-304ea5721819"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x400 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# extra code – this cell generates and saves Figure 3–10\n",
        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
        "plt.rc('font', size=10)\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0],\n",
        "                                        sample_weight=sample_weight,\n",
        "                                        normalize=\"true\", values_format=\".0%\")\n",
        "axs[0].set_title(\"Errors normalized by row\")\n",
        "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1],\n",
        "                                        sample_weight=sample_weight,\n",
        "                                        normalize=\"pred\", values_format=\".0%\")\n",
        "axs[1].set_title(\"Errors normalized by column\")\n",
        "plt.show()\n",
        "plt.rc('font', size=14)  # make fonts great again"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "OnHaT7KT2v09"
      },
      "outputs": [],
      "source": [
        "cl_a, cl_b = '3', '5'\n",
        "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
        "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
        "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
        "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "rOBb5Zoy21jv",
        "outputId": "1eaabe41-ff79-41a4-cd34-0841c3fc7c54"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# extra code – this cell generates and saves Figure 3–11\n",
        "size = 5\n",
        "pad = 0.2\n",
        "plt.figure(figsize=(size, size))\n",
        "for images, (label_col, label_row) in [(X_ba, (0, 0)), (X_bb, (1, 0)),\n",
        "                                       (X_aa, (0, 1)), (X_ab, (1, 1))]:\n",
        "    for idx, image_data in enumerate(images[:size*size]):\n",
        "        x = idx % size + label_col * (size + pad)\n",
        "        y = idx // size + label_row * (size + pad)\n",
        "        plt.imshow(image_data.reshape(28, 28), cmap=\"binary\",\n",
        "                   extent=(x, x + 1, y, y + 1))\n",
        "plt.xticks([size / 2, size + pad + size / 2], [str(cl_a), str(cl_b)])\n",
        "plt.yticks([size / 2, size + pad + size / 2], [str(cl_b), str(cl_a)])\n",
        "plt.plot([size + pad / 2, size + pad / 2], [0, 2 * size + pad], \"k:\")\n",
        "plt.plot([0, 2 * size + pad], [size + pad / 2, size + pad / 2], \"k:\")\n",
        "plt.axis([0, 2 * size + pad, 0, 2 * size + pad])\n",
        "plt.xlabel(\"Predicted label\")\n",
        "plt.ylabel(\"True label\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYZ-za1u5pHN"
      },
      "source": [
        "## Multilabel Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0fPj2nOO5NbH",
        "outputId": "3f4829a4-a504-4dd2-afa3-716e21be5f2c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "y_train_large = (y_train >= '7')\n",
        "y_train_odd = (y_train.astype('int8') % 2 == 1)\n",
        "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
        "\n",
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train, y_multilabel)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "cRU7mwRS7lov",
        "outputId": "bb3b2c2e-056f-4da3-ae19-7610e809180e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[False, False]])"
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kUou7p1X7pk0",
        "outputId": "32e8f1b0-1840-4f50-b0ab-0afd28b96698"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.976410265560605"
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
        "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XV1oHt9WAiSE",
        "outputId": "f110cd2f-a836-4130-de59-5839038070ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9778357403921755"
            ]
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# extra code – shows that we get a negligible performance improvement when we\n",
        "#              set average=\"weighted\" because the classes are already pretty\n",
        "#              well balanced.\n",
        "f1_score(y_multilabel, y_train_knn_pred, average=\"weighted\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "tnslnn1CAj9J",
        "outputId": "acc77fdd-5cb9-4db8-e35a-699e1a825308"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierChain(base_estimator=SVC(), cv=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClassifierChain</label><div class=\"sk-toggleable__content\"><pre>ClassifierChain(base_estimator=SVC(), cv=3, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "ClassifierChain(base_estimator=SVC(), cv=3, random_state=42)"
            ]
          },
          "execution_count": 162,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.multioutput import ClassifierChain\n",
        "\n",
        "chain_clf = ClassifierChain(SVC(), cv=3, random_state=42)\n",
        "chain_clf.fit(X_train[:2000], y_multilabel[:2000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "JQGD6dzQAoYi",
        "outputId": "9f4a6e0a-fd45-4f18-b2b1-349a7a5b2e46"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0., 0.]])"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gunl5NcAwlh"
      },
      "source": [
        "## Multioutput Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "y_tGMNWZAuKa"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)  # to make this code example reproducible\n",
        "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
        "X_train_mod = X_train + noise\n",
        "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
        "X_test_mod = X_test + noise\n",
        "y_train_mod = X_train\n",
        "y_test_mod = X_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3NUILz5dA4mZ",
        "outputId": "66f202be-3d26-44c7-a353-e52c520960a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# extra code – this cell generates and saves Figure 3–12\n",
        "plt.subplot(121); plot_digit(X_test_mod[0])\n",
        "plt.subplot(122); plot_digit(y_test_mod[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "pCFtL68AA_aD",
        "outputId": "f1f8bab4-2934-41ef-95e6-2fcd33e802cb"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAHTUlEQVR4nO3coYuV2wKH4W8fBjUIJuNgMSr4H6jJoMFktNosNstUQRDMgoKgZUwihklGEWxiEkwTxWAY0WG+Wy5vuvees/Y9M3vOzPP0H98qm3evshbzPM8TAEzT9MeqDwDA4SEKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoAJC1VR/gOFgsFsObjY2Npb61vr4+vDl//vzw5vLly8Mb4PBzUwAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFAOKV1APw/v374c3Pnz+X+tbVq1eHN/M8D2+uXbs2vDmKfv36Nbx59+7d33+Q/+LMmTPDmwcPHgxv7ty5M7zhcHJTACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAWczLvIbGobW7uzu8+f79+/Dm2bNnw5uPHz8Ob6ZpmjY3N5fajbp9+/bwZrFYDG++ffs2vJmmaXrz5s1Su1GnTp0a3rx9+3Z4c+XKleEN+89NAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoAxIN48A+xzGOHyzxUd/PmzeHN69evhzc3btwY3rD/3BQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEDWVn0A4K9ZWxv/ue7s7OzDSTjK3BQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYAs5nmeV30IYH9cuHBheLO3tze8+fz58/CGw8lNAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoAZG3VBwD+mg8fPgxvlnmo7v79+8Mbjg43BQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAkMU8z/OqDwH8uT/+OJj/cF+/fh3enDt3bh9Owiq4KQAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgKyt+gBw3Dx//vzAvvXw4cPhjcftjjc3BQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEA/iwQHb2to6sG/du3fvwL7F0eCmAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoAxCup8H/Y2NgY3rx48WKpb3358mWpHYxwUwAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCAFnM8zyv+hBwGHz69Gl4c/HixeHN48ePhzfTNE13795dagcj3BQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEA8iAf/duvWreHNq1evhjc7OzvDm2mappMnTy61gxFuCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIGurPgDsh0ePHg1vNjc3hzdPnjwZ3njYjsPMTQGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAGQxz/O86kPA/7K7uzu8OXHixPBmmZ/Cjx8/hjenT58e3sBBcVMAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQCytuoDwJ85e/bsgXzn6dOnwxsvnnLUuCkAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYB4EI8D8/v376V2169fH968fPlyeHPp0qXhDRw1bgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCLeZ7nVR+C42F7e3up3fr6+t98kv9sb2/vQL4Dh5mbAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAyNqqDwD7YWtra9VHgH8kNwUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCLeZ7nVR+C42F7e3up3fr6+vBmb29vqW/BceemAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUA4kE8AOKmAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAMi/AEUZsuGEdbOsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train_mod, y_train_mod)\n",
        "clean_digit = knn_clf.predict([X_test_mod[0]])\n",
        "plot_digit(clean_digit)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO5X6thH2QSraB2gkNcIo6W",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}