03_Classification.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/shahpranshu27/HandsOn_ML/blob/main/03_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "Here, we will be using the MNIST dataset, which is a set of 70,000 small\n",
        "images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents"
      ],
      "metadata": {
        "id": "YqxzUwNsQzON"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "AkHOg8-QP-_u"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "import matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_openml"
      ],
      "metadata": {
        "id": "geIeNwLpQaAt"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = fetch_openml('mnist_784', version=1)\n",
        "mnist.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkTZ54Q_Qek0",
        "outputId": "b651911d-64b6-4f7a-fb2a-89a1a4ae1bd3"
      },
      "execution_count": 4,
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Datasets loaded by Scikit-Learn generally have a similar dictionary structure, including the following:\n",
        "\n",
        "• A DESCR key describing the dataset\n",
        "\n",
        "• A data key containing an array with one row per instance and one column per\n",
        "feature\n",
        "\n",
        "• A target key containing an array with the labels"
      ],
      "metadata": {
        "id": "ZE8iQ7agRsHc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = mnist[\"data\"], mnist[\"target\"]"
      ],
      "metadata": {
        "id": "23iixWIfQmu0"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the label is string. Most ML algorithms expect numbers, so let's cast y to an integer."
      ],
      "metadata": {
        "id": "Hs9ILG6kdZgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.astype(int)"
      ],
      "metadata": {
        "id": "lZM6CUpwTupT"
      },
      "execution_count": 6,
      "outputs": []
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      "outputs": [
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-847efb4c-56b9-4d88-8918-e1385526a3ec button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-847efb4c-56b9-4d88-8918-e1385526a3ec');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-018c82f3-b1e5-4bb0-b2bf-117abaa3426b button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b623a868-b355-4152-890d-78e86b4c5cba\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b623a868-b355-4152-890d-78e86b4c5cba button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
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              "    </script>\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQAUsRcnR3Er",
        "outputId": "8e377096-60de-48da-cccf-73cb3ed979ae"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUleR2mTR4oK",
        "outputId": "2814694a-c6f8-454d-9fb1-e2768863373f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 70,000 images, and each image has 784 features. This is because each image is 28 x 28 pixels, and each feature simply represents one pixel's intensify, from 0 (white) to 255 (black). Let's take a peek at one digit from the dataset."
      ],
      "metadata": {
        "id": "hZLq1Z-QSHAk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "some_digit = X.iloc[0]  # Take the first digit\n",
        "some_digit_image = some_digit.values.reshape(28, 28)  # Reshape to 28x28 array\n",
        "\n",
        "plt.imshow(some_digit_image, cmap=\"binary\")\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "3e2olemsR6ey",
        "outputId": "a11f1058-d2a4-4bbe-856e-6da8b4e9e893"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWgNWA7CS5DK",
        "outputId": "f73ef732-3464-4c37-97cd-e270e2b625ef"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_digit(data):\n",
        "  image = data.reshape(28,28)\n",
        "  plt.imshow(image, cmap = matplotlib.cm.binary,\n",
        "             interpolation=\"nearest\")\n",
        "  plt.axis(\"off\")"
      ],
      "metadata": {
        "id": "VA1Ttqj9d1Lw"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def plot_digits(instances, images_per_row=10, **options):\n",
        "  size = 28\n",
        "  images_per_row = min(len(instances), images_per_row)\n",
        "  n_rows = math.ceil(len(instances)/images_per_row)\n",
        "\n",
        "  # Append empty images to fill the end of the grid, if needed\n",
        "  n_empty = n_rows * images_per_row - len(instances)\n",
        "  padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
        "\n",
        "  #Reshape the array, so it's organized as a grid containing 28x28 images:\n",
        "  image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
        "\n",
        "  # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis)\n",
        "  # and axes 1 and 3 (horizontal axes). We first need to move the axes that we want to combine\n",
        "  # next to each other, using transpose(), and only then we can reshape:\n",
        "  big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size, images_per_row * size)\n",
        "\n",
        "  # Now that we have a big image, we just need to show it:\n",
        "  plt.imshow(big_image, cmap = matplotlib.cm.binary, **options)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "QQ7jubkreG8f"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "example_images = X[:100]\n",
        "plot_digits(example_images, images_per_row=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "lYvUuE6pfkO4",
        "outputId": "9fd2c7fa-ff7c-4cc3-e3fe-1a8752ad891e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The MNIST dataset is actually already split into a training set (the first 60,000\n",
        "images) and a test set (the last 10,000 images)"
      ],
      "metadata": {
        "id": "yg0t7B43fxJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
      ],
      "metadata": {
        "id": "jXhSfOx7WCCf"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's shuffle the training set, because this guarantees that\n",
        "all cross-validation folds will be similar (you don’t want one fold to be missing some\n",
        "digits)."
      ],
      "metadata": {
        "id": "zveys0Cef8Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shuffle_index = np.random.permutation(60000)\n",
        "X_train, y_train = X_train.iloc[shuffle_index], y_train[shuffle_index]"
      ],
      "metadata": {
        "id": "Cl2LztgKdk1W"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training a Binary Classifier"
      ],
      "metadata": {
        "id": "erMhtR3ugtMi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s simplify the problem for now and only try to identify one digit—for example,\n",
        "the number 5. This “5-detector” will be an example of a binary classifier, capable of\n",
        "distinguishing between just two classes, 5 and not-5"
      ],
      "metadata": {
        "id": "3sY3Jqwgg3ji"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create the target vectors for this classification task:"
      ],
      "metadata": {
        "id": "nub_FTZ8g5tq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_5 = (y_train == 5)\n",
        "y_test_5 = (y_test == 5)"
      ],
      "metadata": {
        "id": "jgtjjBs1gNii"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let’s pick a classifier and train it. A good place to start is with a Stochastic Gradient Descent (SGD) classifier, using Scikit-Learn’s `SGDClassifier` class. This classifier\n",
        "has the advantage of being capable of handling very large datasets efficiently. This is\n",
        "in part because SGD deals with training instances independently, one at a time\n",
        "(which also makes SGD well suited for online learning)."
      ],
      "metadata": {
        "id": "G5kkR84Uhe27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "metadata": {
        "id": "NETtRln5hLJh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sgd_clf = SGDClassifier(random_state=42)\n",
        "sgd_clf.fit(X_train, y_train_5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "GFvllM4fhqLw",
        "outputId": "f64ff82a-9e79-47a2-948c-74054be58c40"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `SGDClassifier` relies on randomness during training (hence\n",
        "the name “stochastic”). If you want reproducible results, you\n",
        "should set the `random_state` parameter."
      ],
      "metadata": {
        "id": "3qJaMtUIiGFq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sgd_clf.predict([some_digit])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jgzpk-EIhzug",
        "outputId": "9247a5d1-d049-4ece-8d56-5c860f02c456"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The classifier guesses that this image represents a 5 (True). Looks like it guessed right\n",
        "in this particular case! Now, let’s evaluate this model’s performance."
      ],
      "metadata": {
        "id": "AbMTakKjibF6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance Measures"
      ],
      "metadata": {
        "id": "ofmBiO2hicxp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluating a classifier is often significantly trickier than evaluating a regressor, so we will spend a large part on this topic."
      ],
      "metadata": {
        "id": "99ujwqSzimAS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Measuring Accuracy Using Cross-Validation"
      ],
      "metadata": {
        "id": "t4iNn8oZitg2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### (Extra) Implementing Cross-Validation\n",
        "\n",
        "Occasionally you will need more control over the cross-validation process than what\n",
        "Scikit-Learn provides off the shelf. In these cases, you can implement cross-validation\n",
        "yourself. The following code does roughly the same thing as Scikit-Learn’s\n",
        "`cross_val_score()` function, and it prints the same result:\n"
      ],
      "metadata": {
        "id": "2KP4wnxfi1Iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.base import clone"
      ],
      "metadata": {
        "id": "TWkAU_YBiPdY"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
        "\n",
        "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
        "  clone_clf = clone(sgd_clf)\n",
        "  X_train_folds = X_train.iloc[train_index]\n",
        "  y_train_folds = y_train_5[train_index]\n",
        "  X_test_fold = X_train.iloc[test_index]\n",
        "  y_test_fold = y_train_5[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))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wqt1p8MZjJUP",
        "outputId": "8184f5fe-e976-48ab-a215-2964096cd732"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.81315\n",
            "0.90155\n",
            "0.67865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `StratifiedKFold` class performs stratified sampling to produce folds that contain a representative ratio of each class. At each iteration the\n",
        "code creates a clone of the classifier, trains that clone on the training folds, and makes\n",
        "predictions on the test fold. Then it counts the number of correct predictions and\n",
        "outputs the ratio of correct predictions.\n"
      ],
      "metadata": {
        "id": "OzEbR0fHkLOR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s use the `cross_val_score()` function to evaluate our SGDClassifier model,\n",
        "using K-fold cross-validation with three folds. Remember that K-fold cross-validation\n",
        "means splitting the training set into K folds (in this case, three), then making predictions and evaluating them on each fold using a model trained on the remaining folds:"
      ],
      "metadata": {
        "id": "TSojIKVLkSqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4J3Xz_ujeJX",
        "outputId": "ad6f7f39-f25d-42a0-8943-f6a21d01565a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96575, 0.96425, 0.9539 ])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wow! Above 95% accuracy (ratio of correct predictions) on all cross-validation folds?\n",
        "This looks amazing, doesn’t it? Well, before you get too excited, let’s look at a very\n",
        "dumb classifier that just classifies every single image in the “not-5” class:"
      ],
      "metadata": {
        "id": "jxxw1aebk47h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator"
      ],
      "metadata": {
        "id": "WR44VW-Pki2W"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Never5Classifier(BaseEstimator):\n",
        "  def fit(self, X, y=None):\n",
        "    pass\n",
        "  def predict(self, X):\n",
        "    return np.zeros((len(X), 1), dtype=bool)"
      ],
      "metadata": {
        "id": "OznTn_JUuNJz"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "never_5_clf = Never5Classifier()\n",
        "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDZAjJncu8Kr",
        "outputId": "250b0ae5-e686-465f-abce-14ca6520168b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.90815, 0.9094 , 0.9114 ])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That’s right, it has over 90% accuracy! This is simply because only about 10% of the\n",
        "images are 5s, so if you always guess that an image is not a 5, you will be right about\n",
        "90% of the time.\n",
        "\n",
        "This demonstrates why accuracy is generally not the preferred performance measure\n",
        "for classifiers, especially when you are dealing with skewed datasets (i.e., when some\n",
        "classes are much more frequent than others)."
      ],
      "metadata": {
        "id": "RCIBS1KQvuXd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrix"
      ],
      "metadata": {
        "id": "8LTIv4rJwSXD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A much better way to evaluate the performance of a classifier is to look at the confusion matrix. The general idea is to count the number of times instances of class A are\n",
        "classified as class B. For example, to know the number of times the classifier confused\n",
        "images of 5s with 3s, you would look in the fifth row and third column of the confusion matrix.\n",
        "To compute the confusion matrix, you first need to have a set of predictions so that\n",
        "they can be compared to the actual targets. You could make predictions on the test\n",
        "set, but let’s keep it untouched for now (remember that you want to use the test set\n",
        "only at the very end of your project, once you have a classifier that you are ready to\n",
        "launch). Instead, you can use the cross_val_predict() function:"
      ],
      "metadata": {
        "id": "LHbuHNf_wVW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict"
      ],
      "metadata": {
        "id": "QXdfV47UvPvU"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
      ],
      "metadata": {
        "id": "_dXygwLZw9VC"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just like the cross_val_score() function, cross_val_predict() performs K-fold\n",
        "cross-validation, but instead of returning the evaluation scores, it returns the predictions made on each test fold. This means that you get a clean prediction for each\n",
        "instance in the training set (“clean” meaning that the prediction is made by a model\n",
        "that never saw the data during training).\n",
        "Now you are ready to get the confusion matrix using the confusion_matrix() function. Just pass it the target classes (y_train_5) and the predicted classes\n",
        "(y_train_pred):"
      ],
      "metadata": {
        "id": "zswUHqIMx5yU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "EdTlvTkUxEky"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_train_5, y_train_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQeD-Bo6yAPZ",
        "outputId": "2519e4df-cf9a-4d28-963f-13352e2906f9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[53590,   989],\n",
              "       [ 1333,  4088]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each row in a confusion matrix represents an actual class, while each column represents a predicted class. The first row of this matrix considers non-5 images (the negative class): 53,590 of them were correctly classified as non-5s (they are called true\n",
        "negatives), while the remaining 989 were wrongly classified as 5s (false positives).\n",
        "The second row considers the images of 5s (the positive class): 1,333 were wrongly\n",
        "classified as non-5s (false negatives), while the remaining 4,088 were correctly classified as 5s (true positives). A perfect classifier would have only true positives and true\n",
        "negatives, so its confusion matrix would have nonzero values only on its main diagonal (top left to bottom right):"
      ],
      "metadata": {
        "id": "TpaIOLIJ8Amv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_perfect_predictions = y_train_5\n",
        "confusion_matrix(y_train_5, y_train_perfect_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "218WvQXdyQJC",
        "outputId": "b5ce7c46-aeb2-4434-c8db-8c509d523c80"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[54579,     0],\n",
              "       [    0,  5421]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrix gives you a lot of information, but sometimes you may prefer a\n",
        "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",
        "\n",
        "> precision = TP/(TP + FP)\n",
        "\n",
        "TP is the number of true positives, and FP is the number of false positives\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "A trivial way to have perfect precision is to make one single positive prediction and\n",
        "ensure it is correct (precision = 1/1 = 100%). But this would not be very useful, since\n",
        "the classifier would ignore all but one positive instance. So precision is typically used\n",
        "along with another metric named recall, also called sensitivity or the true positive rate\n",
        "(TPR): this is the ratio of positive instances that are correctly detected by the classifier.\n",
        "\n",
        "> recall = TP/(TP + FN)\n",
        "\n",
        "FN is, of course, the number of false negatives."
      ],
      "metadata": {
        "id": "TM7bFy239Oze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Precision and Accuracy"
      ],
      "metadata": {
        "id": "74eIUJAS-pUd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score"
      ],
      "metadata": {
        "id": "x6yy90jk8c-0"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(y_train_5, y_train_pred) # == 4088 / (4088+989)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5o2IU-H-vyz",
        "outputId": "80eb2d7b-ee4b-426d-a09c-836ac188efd1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.805199921213315"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_score(y_train_5, y_train_pred) # == 4088 / (4088 + 1333)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4niEOvrH-zQS",
        "outputId": "ac4f1b33-ab9d-4e0a-c91b-1719343e65d7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7541044087806678"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now your 5-detector does not look as shiny as it did when you looked at its accuracy.\n",
        "When it claims an image represents a 5, it is correct only 80.5% of the time. Moreover, it only detects 75.4% of the 5s."
      ],
      "metadata": {
        "id": "h9xLhXNv_aF9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is often convenient to combine precision and recall into a single metric called the F1\n",
        "score, in particular if you need a simple way to compare two classifiers. The F1\n",
        " score is\n",
        "the harmonic mean of precision and recall. Whereas the regular mean\n",
        "treats all values equally, the harmonic mean gives much more weight to low values.\n",
        "As a result, the classifier will only get a high F1\n",
        " score if both recall and precision are\n",
        "high.\n",
        "\n",
        "> F1 = 2 / ((1/precision) + (1/recall))\n",
        "\n",
        "To compute the F1\n",
        " score, simply call the f1_score() function:"
      ],
      "metadata": {
        "id": "WThs4PD__4gU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "metadata": {
        "id": "D5f1sXso-4rb"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_train_5, y_train_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KUi0GGOAgkS",
        "outputId": "4720cb8b-a432-418a-94a4-3bf88107e06e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.778815012383311"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The F1\n",
        "score favors classifiers that have similar precision and recall. This is not always\n",
        "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\n",
        "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\n",
        "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\n",
        "shoplifters in surveillance images: it is probably fine if your classifier has only 30%\n",
        "precision as long as it has 99% recall (sure, the security guards will get a few false\n",
        "alerts, but almost all shoplifters will get caught).\n",
        "Unfortunately, you can’t have it both ways: increasing precision reduces recall, and\n",
        "vice versa. This is called the `precision`/`recall trade-off`."
      ],
      "metadata": {
        "id": "PLWd8_YqA1Xc"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UJKHgV49AiuB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}