chapter_3_chandra.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/chapter_3_chandra.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BohbozBSeKi7"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0IrL5O97fAEj"
      },
      "outputs": [],
      "source": [
        "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": "9oww0XYkfBqF"
      },
      "outputs": [],
      "source": [
        "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": "code",
      "execution_count": null,
      "metadata": {
        "id": "pzITS3CJfDCm"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Define the path to save images for classification\n",
        "IMAGES_PATH = Path() / \"images\" / \"classification\"\n",
        "# Create the directory if it doesn't exist\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4YQh9FuZfEyL"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vw_O8u9YfFjw"
      },
      "source": [
        "## MNIST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eImSBzPfGpW",
        "outputId": "526fa257-b1fb-4dc4-b8e6-c9806bc898c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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",
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:65: RuntimeWarning: Invalid cache, redownloading file\n",
            "  warn(\"Invalid cache, redownloading file\", RuntimeWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:100: UserWarning: A network error occurred while downloading https://openml.org/api/v1/json/data/qualities/554. Retrying...\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "# Fetch the MNIST dataset from OpenML\n",
        "# The dataset contains 70,000 images of handwritten digits (0-9)\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": "CYcAB6btfH8z",
        "outputId": "a493d5c0-5cb9-4287-adf0-3a6cc986dacb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Display the keys of the MNIST dataset dictionary\n",
        "mnist.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aw1XNqatfMeL",
        "outputId": "60282360-68b5-4372-acbb-4b4a09f04421"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Assign the data and target from the MNIST dataset to variables X and y\n",
        "X, y = mnist.data, mnist.target\n",
        "# Display the data (features)\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKOG8by3fNnj",
        "outputId": "a987bb4e-7708-4892-c300-959d2a824a01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwLcuJCefOxr",
        "outputId": "509b1a00-4f10-42e0-a37c-b1b11af3e290"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['5', '0', '4', ..., '4', '5', '6'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "## Display the target values (labels)\n",
        "y\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4JJ7IerfP--",
        "outputId": "d7462bd9-b847-434c-92cd-b174b89c3ceb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuAH6jsQfRzm",
        "outputId": "acf6cb99-6e21-4860-90a1-e5278186d230"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "784"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Each image in the MNIST dataset is 28x28 pixels, resulting in 784 features\n",
        "28 * 28"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "UArAjv92gmCO",
        "outputId": "25127c09-c03e-4306-f07b-18ecd2c50459"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to plot a single digit image\n",
        "def plot_digit(image_data):\n",
        "    # Reshape the image data to 28x28 pixels\n",
        "    image = image_data.reshape(28, 28)\n",
        "    # Display the image using a binary color map (black and white)\n",
        "    plt.imshow(image, cmap=\"binary\")\n",
        "    # Turn off the axis\n",
        "    plt.axis(\"off\")\n",
        "\n",
        "# Select the first image from the dataset\n",
        "some_digit = X[0]\n",
        "# Plot the selected digit\n",
        "plot_digit(some_digit)\n",
        "# Save the plot\n",
        "save_fig(\"some_digit_plot\")\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RtPnn_x7gzhN",
        "outputId": "50823d28-f8e9-45e1-fd29-5e89d17f4ad1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Display the label of the first image in the dataset\n",
        "y[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "i8vyqcT_hBAC",
        "outputId": "c7a2a966-0263-4792-b0c1-bf3a04962266"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 100 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a figure with a size of 9x9 inches\n",
        "plt.figure(figsize=(9, 9))\n",
        "\n",
        "# Plot the first 100 images in a 10x10 grid\n",
        "for idx, image_data in enumerate(X[:100]):\n",
        "    plt.subplot(10, 10, idx + 1)\n",
        "    plot_digit(image_data)\n",
        "\n",
        "# Adjust the layout to remove spacing between subplots\n",
        "plt.subplots_adjust(wspace=0, hspace=0)\n",
        "\n",
        "# Save the plot\n",
        "save_fig(\"more_digits_plot\", tight_layout=False)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ip1V3p-mjFMF"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and test sets\n",
        "# The first 60,000 samples are used for training\n",
        "# The remaining 10,000 samples are used for testing\n",
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3xeEfTxizGJ"
      },
      "source": [
        "### Training a Binary Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xYfAHAGVi2j1"
      },
      "outputs": [],
      "source": [
        "# Create target vectors for binary classification\n",
        "# y_train_5 is True for all training samples labeled as '5', False otherwise\n",
        "# y_test_5 is True for all test samples labeled as '5', False otherwise\n",
        "y_train_5 = (y_train == '5')\n",
        "y_test_5 = (y_test == '5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "uWdOjHRGjSGN",
        "outputId": "840c546c-3dc5-45ba-fb08-983345d0aa01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" 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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" 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>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "# Initialize the Stochastic Gradient Descent (SGD) classifier\n",
        "sgd_clf = SGDClassifier(random_state=42)\n",
        "\n",
        "# Train the SGD classifier on the training data to detect the digit '5'\n",
        "sgd_clf.fit(X_train, y_train_5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sgd_clf.predict([some_digit])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOES5jz5SkL2",
        "outputId": "1db8c3a8-bda6-4d17-82ec-71e497df048c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Additional model (KNN)"
      ],
      "metadata": {
        "id": "92q4bJKXR7tt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Create target vectors for binary classification\n",
        "# y_train_5 is True for all training samples labeled as '5', False otherwise\n",
        "# y_test_5 is True for all test samples labeled as '5', False otherwise\n",
        "y_train_5 = (y_train == '5')\n",
        "y_test_5 = (y_test == '5')\n",
        "\n",
        "# Initialize the KNN classifier with k=3 (you can choose different values for k)\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# Train the KNN classifier\n",
        "knn_clf.fit(X_train, y_train_5)\n",
        "\n",
        "# Make predictions on the test data using KNN\n",
        "y_test_pred_knn = knn_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the KNN model's performance\n",
        "accuracy_knn = accuracy_score(y_test_5, y_test_pred_knn)\n",
        "conf_matrix_knn = confusion_matrix(y_test_5, y_test_pred_knn)\n",
        "class_report_knn = classification_report(y_test_5, y_test_pred_knn)\n",
        "\n",
        "# Print the evaluation metrics for KNN\n",
        "print(\"KNN Classifier:\")\n",
        "print(f\"Accuracy: {accuracy_knn}\")\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix_knn)\n",
        "print(\"Classification Report:\")\n",
        "print(class_report_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbBls4e-K-9P",
        "outputId": "afe03056-f5a4-4e4e-eab9-709ec95301af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Classifier:\n",
            "Accuracy: 0.9938\n",
            "Confusion Matrix:\n",
            "[[9080   28]\n",
            " [  34  858]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       1.00      1.00      1.00      9108\n",
            "        True       0.97      0.96      0.97       892\n",
            "\n",
            "    accuracy                           0.99     10000\n",
            "   macro avg       0.98      0.98      0.98     10000\n",
            "weighted avg       0.99      0.99      0.99     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the confusion matrix\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(conf_matrix_knn, annot=True, fmt='d', cmap='Blues', xticklabels=['Not 5', '5'], yticklabels=['Not 5', '5'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix for KNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "ozT83Do6MH1L",
        "outputId": "f7eb5d03-f99f-4359-8051-5b181284e733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrix for the KNN classifier is a 2x2 grid that summarizes the performance of the classifier by comparing the predicted labels with the actual labels. Here’s an explanation of each square:\n",
        "\n",
        "### Confusion Matrix Breakdown\n",
        "\n",
        "\\[\n",
        "\\begin{array}{|c|c|c|}\n",
        "\\hline\n",
        "\\text{} & \\text{Predicted Not 5} & \\text{Predicted 5} \\\\\n",
        "\\hline\n",
        "\\text{Actual Not 5} & 9080 & 28 \\\\\n",
        "\\hline\n",
        "\\text{Actual 5} & 34 & 858 \\\\\n",
        "\\hline\n",
        "\\end{array}\n",
        "\\]\n",
        "\n",
        "### Explanation of Each Square:\n",
        "\n",
        "1. **Top-left square (9080): True Negatives (TN)**\n",
        "   - **Description:** This represents the number of samples that are actually not '5' and were correctly predicted as not '5' by the classifier.\n",
        "   - **Significance:** High values here indicate that the classifier is good at identifying samples that are not '5'.\n",
        "\n",
        "2. **Top-right square (28): False Positives (FP)**\n",
        "   - **Description:** This represents the number of samples that are actually not '5' but were incorrectly predicted as '5' by the classifier.\n",
        "   - **Significance:** Lower values here are better, as it means the classifier is making fewer mistakes by incorrectly labeling non-'5' samples as '5'.\n",
        "\n",
        "3. **Bottom-left square (34): False Negatives (FN)**\n",
        "   - **Description:** This represents the number of samples that are actually '5' but were incorrectly predicted as not '5' by the classifier.\n",
        "   - **Significance:** Lower values here are better, as it means the classifier is making fewer mistakes by failing to identify '5' samples.\n",
        "\n",
        "4. **Bottom-right square (858): True Positives (TP)**\n",
        "   - **Description:** This represents the number of samples that are actually '5' and were correctly predicted as '5' by the classifier.\n",
        "   - **Significance:** High values here indicate that the classifier is good at correctly identifying samples that are '5'.\n",
        "\n",
        "### Summary\n",
        "- **True Negatives (TN = 9080):** The classifier correctly identified 9080 samples as not '5'.\n",
        "- **False Positives (FP = 28):** The classifier incorrectly identified 28 samples as '5' when they were not '5'.\n",
        "- **False Negatives (FN = 34):** The classifier incorrectly identified 34 samples as not '5' when they were actually '5'.\n",
        "- **True Positives (TP = 858):** The classifier correctly identified 858 samples as '5'."
      ],
      "metadata": {
        "id": "HC1-G2quRNUg"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbGKNhvskmgQ"
      },
      "source": [
        "## Performance measures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lyn1VSWckrBy"
      },
      "source": [
        "### Measuring Accuracy Using Cross-Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxjaxzvTkqRL",
        "outputId": "7e4e2179-ade4-44a3-b301-d597397cba89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.95035, 0.96035, 0.9604 ])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Perform cross-validation for the SGDClassifier\n",
        "# The accuracy score will be calculated using 3-fold cross-validation\n",
        "sgd_accuracy_scores = cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n",
        "\n",
        "# Display the cross-validation accuracy scores\n",
        "sgd_accuracy_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33UOSddpqs63",
        "outputId": "0b261fed-dd06-45fe-e1e6-c6f2855652ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.95035\n",
            "0.96035\n",
            "0.9604\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.base import clone\n",
        "\n",
        "# Initialize the StratifiedKFold with 3 splits\n",
        "# Add shuffle=True if the dataset is not already shuffled\n",
        "skfolds = StratifiedKFold(n_splits=3)\n",
        "\n",
        "# Perform Stratified K-Fold cross-validation\n",
        "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
        "    # Clone the classifier to ensure a fresh model for each fold\n",
        "    clone_clf = clone(sgd_clf)\n",
        "\n",
        "    # Create the training and testing folds\n",
        "    X_train_folds = X_train[train_index]\n",
        "    y_train_folds = y_train_5[train_index]\n",
        "    X_test_fold = X_train[test_index]\n",
        "    y_test_fold = y_train_5[test_index]\n",
        "\n",
        "    # Train the classifier on the training fold\n",
        "    clone_clf.fit(X_train_folds, y_train_folds)\n",
        "\n",
        "    # Predict on the test fold\n",
        "    y_pred = clone_clf.predict(X_test_fold)\n",
        "\n",
        "    # Calculate the number of correct predictions\n",
        "    n_correct = sum(y_pred == y_test_fold)\n",
        "\n",
        "    # Print the accuracy for this fold\n",
        "    print(n_correct / len(y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "He6jr-25ubWi"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VujtJ4__rNVX",
        "outputId": "806eefc7-e3ef-45b8-b204-c4978b74087b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        }
      ],
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "# Initialize the DummyClassifier\n",
        "dummy_clf = DummyClassifier()\n",
        "\n",
        "# Train the DummyClassifier on the training data\n",
        "dummy_clf.fit(X_train, y_train_5)\n",
        "\n",
        "# Predict on the training data and check if any prediction is True\n",
        "# This checks if the DummyClassifier predicts any positive class\n",
        "print(any(dummy_clf.predict(X_train)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbxHEscmraUM",
        "outputId": "ecc33a31-b40b-4d1b-8b7c-301c8dee38eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.90965, 0.90965, 0.90965])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Perform cross-validation for the DummyClassifier\n",
        "# The accuracy score will be calculated using 3-fold cross-validation\n",
        "dummy_accuracy_scores = cross_val_score(dummy_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n",
        "\n",
        "# Display the cross-validation accuracy scores for DummyClassifier\n",
        "dummy_accuracy_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZVjkJUFMrlW7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_TachPvrni2"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aXz7QRClrpIm"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "# Perform cross-validation predictions for the SGDClassifier\n",
        "# The target predictions will be calculated using 3-fold cross-validation\n",
        "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZBfOjQUr1Aq",
        "outputId": "236d603e-f3c7-494b-cb8c-a36b1dbcd8c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[53892,   687],\n",
              "       [ 1891,  3530]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Import the confusion_matrix function from the sklearn.metrics module\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Compute the confusion matrix using the actual labels (y_train_5) and the predicted labels (y_train_pred)\n",
        "# y_train_5: Actual labels\n",
        "# y_train_pred: Predicted labels\n",
        "cm = confusion_matrix(y_train_5, y_train_pred)\n",
        "\n",
        "# Print the confusion matrix\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYiYGhFPr2ct",
        "outputId": "38eda89e-87b3-4e30-89a7-ca38e3682c5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[54579,     0],\n",
              "       [    0,  5421]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Assigning y_train_5 to y_train_perfect_predictions to pretend perfect predictions\n",
        "y_train_perfect_predictions = y_train_5\n",
        "\n",
        "# Compute the confusion matrix using the actual labels (y_train_5) and the perfect predicted labels (y_train_perfect_predictions)\n",
        "# y_train_5: Actual labels\n",
        "# y_train_perfect_predictions: Perfectly predicted labels (same as actual labels)\n",
        "confusion_matrix(y_train_5, y_train_perfect_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Kr3zZl_uiJ9"
      },
      "source": [
        "## Precision and Recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYFiURn0ukZu",
        "outputId": "91846143-f3c7-4747-e9ba-a8395dfaf001"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8370879772350012"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "precision_score(y_train_5, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6cMsFL6vcLG",
        "outputId": "87ee42de-8671-47f5-d6e6-2a2fea810214"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6511713705958311"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "recall_score(y_train_5, y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGUuDfEHvkMU",
        "outputId": "6745ffb4-1e8d-4d78-c07b-506f9f340187"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7325171197343846"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "f1_score(y_train_5, y_train_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9KLiO_dv33m"
      },
      "source": [
        "### Precision/Recall Trade-off"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2yPK3Dfu2o_J",
        "outputId": "388e9f22-5f02-4fe9-f72c-9b9c31da8b88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
              "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
              "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
              "        -12050.767298  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "# Select a digit from the dataset\n",
        "some_digit = X[0]\n",
        "\n",
        "# Calculate the decision score for some_digit\n",
        "# Positive score: classifier predicts '5'\n",
        "# Negative score: classifier predicts not '5'\n",
        "y_scores = sgd_clf.decision_function([some_digit])\n",
        "\n",
        "# Print the decision score\n",
        "y_scores\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C6nR_XHS4pi9"
      },
      "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": "1IQFBe496T-w",
        "outputId": "e77d5a45-bb7f-4c17-d418-2e58b7db8981"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HOc9n0a27LdT",
        "outputId": "f0674e1a-a6f4-40ec-e960-c185e1a8ea9d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "threshold = 3000\n",
        "y_some_digit_pred = (y_scores > threshold)\n",
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8DLaHmHv7NQE"
      },
      "outputs": [],
      "source": [
        "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
        "                             method=\"decision_function\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j0CuNpYD7Owe"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "eY7Vq1JI7ZKt",
        "outputId": "a67c3e7b-8537-493b-cf09-085802c0988f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\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",
        "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",
        "save_fig(\"precision_recall_vs_threshold_plot\")\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "gJF7yMj17dyU",
        "outputId": "03e0d0f6-7a52-48d0-8358-554aba0bd2b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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 and saves Figure 3–6\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",
        "save_fig(\"precision_vs_recall_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSOuSstW7gaD",
        "outputId": "d76990f5-6dab-4b1a-fef0-d7cf08629fc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3370.0194991439557"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "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": "KA4whrUv7iTv"
      },
      "outputs": [],
      "source": [
        "y_train_pred_90 = (y_scores >= threshold_for_90_precision)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HocNq57P7kad",
        "outputId": "8f29d6eb-52c2-4002-f833-e96d1297cb6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9000345901072293"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "precision_score(y_train_5, y_train_pred_90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stsBjQBI7l4o",
        "outputId": "b3b9f559-6de4-4660-b8eb-153d0ce7187c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4799852425751706"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "recall_at_90_precision = recall_score(y_train_5, y_train_pred_90)\n",
        "recall_at_90_precision\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UPzenx5G7n04"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFwWQlDk7prx"
      },
      "source": [
        "## The ROC Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hq8AdcM67tAf"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "kQ34Gb0t7uX9",
        "outputId": "8b9e8171-f85c-488d-e396-08037603286b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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",
        "save_fig(\"roc_curve_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFPj0Ycv7v7W",
        "outputId": "14248c12-42e5-41e2-93f3-dcb62bcff60d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9604938554008616"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(y_train_5, y_scores)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OpNvq8SP7zAl"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest_clf = RandomForestClassifier(random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pV_rYDjs70wj"
      },
      "outputs": [],
      "source": [
        "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
        "                                    method=\"predict_proba\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2jfHVME72Uj",
        "outputId": "6d5918e2-ef37-4b1f-dba5-62bb43b75ae9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.11, 0.89],\n",
              "       [0.99, 0.01]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "y_probas_forest[:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezvH6sfX8bi3"
      },
      "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": "ryuF3zL28Msy",
        "outputId": "7947ceb1-93e2-4a37-e284-a5229ab9983c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.0%\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_5[idx_50_to_60]).sum() / idx_50_to_60.sum():.1%}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5L7RuLnU8fpN"
      },
      "outputs": [],
      "source": [
        "y_scores_forest = y_probas_forest[:, 1]\n",
        "precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
        "    y_train_5, y_scores_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "VGLP_2Fz8hSZ",
        "outputId": "6ea3fc70-0ef4-48ce-d7ab-ecaf3b6dd366"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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",
        "save_fig(\"pr_curve_comparison_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYiZMjdd8nty"
      },
      "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": "XQOrMbQh8ixC",
        "outputId": "6cc832c1-72d2-464b-f295-c73e840d6047"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9274509803921569"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5  # positive proba ≥ 50%\n",
        "f1_score(y_train_5, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkQV_H0e8raN",
        "outputId": "0ed5c577-7cf8-4642-c881-8ae30080b133"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9983436731328145"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "roc_auc_score(y_train_5, y_scores_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07dgJEES8sjr",
        "outputId": "8935eae4-9e98-4f09-e64c-adbf02376da5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9897468089558485"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "precision_score(y_train_5, y_train_pred_forest)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TY-O40Oi8vEg",
        "outputId": "51f0bec8-61df-431a-e502-29b36dd666c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8725327430363402"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "recall_score(y_train_5, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SyoYKtfm840u"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGF2OvYd85bI"
      },
      "source": [
        "### Multiclass Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "9JUenjE4876h",
        "outputId": "243b9a70-d929-4f62-cbec-d5f36963ea78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKokmG5g9A_P",
        "outputId": "fff1ebef-bab5-4ad7-afd9-631fadf2416b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['5'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "svm_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHgSwMxu9CiL",
        "outputId": "fa8fcc58-e002-45fb-f32f-1892589fc166"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.79,  0.73,  6.06,  8.3 , -0.29,  9.3 ,  1.75,  2.77,  7.21,\n",
              "         4.82]])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "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": "q-93AMo49D4g",
        "outputId": "34145458-b232-4419-c72a-6c56e586f513"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "class_id = some_digit_scores.argmax()\n",
        "class_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBmro86U9FGT",
        "outputId": "8ae68234-672d-4868-a21d-05291c7a7618"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "svm_clf.classes_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Rx14umxA9GS5",
        "outputId": "ea9f0204-9074-421c-e1a2-16375b072bb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "svm_clf.classes_[class_id]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTt_kzN29IEp",
        "outputId": "1a1dee20-37da-4780-f1e6-347068a46847"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.11, -0.21, -0.97,  0.51, -1.01,  0.19,  0.09, -0.31, -0.04,\n",
              "        -0.45, -1.28,  0.25, -1.01, -0.13, -0.32, -0.9 , -0.36, -0.93,\n",
              "         0.79, -1.  ,  0.45,  0.24, -0.24,  0.25,  1.54, -0.77,  1.11,\n",
              "         1.13,  1.04,  1.2 , -1.42, -0.53, -0.45, -0.99, -0.95,  1.21,\n",
              "         1.  ,  1.  ,  1.08, -0.02, -0.67, -0.14, -0.3 , -0.13,  0.25]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "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": 115
        },
        "id": "5Rq5q5bt9Pz8",
        "outputId": "b47eb813-4397-4001-cea1-77c02d1d0811"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=SVC(random_state=42))"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "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": "Dl22WaoS9RqA",
        "outputId": "86b4e464-ef07-4b15-ff88-fdfee9386e10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['5'], dtype='<U1')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "ovr_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEhpYr7P9TEa",
        "outputId": "10d0aa4d-9750-4a5d-c79f-2e5c0e624d9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "len(ovr_clf.estimators_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iF4aoMAB9UXa",
        "outputId": "9a257e5b-d73b-4936-9531-a069bb886d80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['3'], dtype='<U1')"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "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": "_aDpIHgw9V11",
        "outputId": "8bba89f0-38e8-4d68-9c33-fd6bcba4b1f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-31893., -34420.,  -9531.,   1824., -22320.,  -1386., -26189.,\n",
              "        -16148.,  -4604., -12051.]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "sgd_clf.decision_function([some_digit]).round()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCEaLfGn9XLe",
        "outputId": "d6a376f3-eb7d-4d42-96a9-4abda54d5ead"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.87365, 0.85835, 0.8689 ])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZwUz54qF9Yj3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "864847ea-a8e6-4001-cb9b-e94795458c36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.8983, 0.891 , 0.9018])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "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": "code",
      "execution_count": null,
      "metadata": {
        "id": "YPm-lJiw9aZB"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "authorship_tag": "ABX9TyNPVgv64T34R/E2tpDC/EdI",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}