Support_Vector_Machines.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "47f2fd8c",
      "metadata": {
        "id": "47f2fd8c"
      },
      "outputs": [],
      "source": [
        "from utils.utils import get_json_content\n",
        "from utils.learners import execute_a_series_of_support_vector_machine_learnings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16c39265",
      "metadata": {
        "id": "16c39265"
      },
      "source": [
        "# Support Vector Machine Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb1811bd",
      "metadata": {
        "id": "eb1811bd"
      },
      "source": [
        "## File: 'headlines_001.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56597284",
      "metadata": {
        "id": "56597284"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "fded15dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fded15dd",
        "outputId": "271c8ada-ecc0-4110-d3ed-980ec2e1d667"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LENGTH - headline: 1000; is_clickbait: 1000\n"
          ]
        }
      ],
      "source": [
        "# Reads the data from the file\n",
        "X, Y = get_json_content(i=0)\n",
        "\n",
        "# Prints the dataset size\n",
        "print(f\"LENGTH - headline: {len(X)}; is_clickbait: {len(Y)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c16def4a",
      "metadata": {
        "id": "c16def4a"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "df731dd7",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "df731dd7",
        "outputId": "0d9dfedd-87b8-4687-c567-6a1b96bffbcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "Precomputed matrix must be a square matrix. Input is a 700x3288 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: sigmoid\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 68.0 %\n",
            "\n",
            "Average precision score: 61.547034047034046 %\n",
            "\n",
            "Average recall score: 69.5945945945946 %\n",
            "\n",
            "Average F1-score: 68.21192052980133 %\n",
            "\n",
            "Matthews correlation coefficient: 0.3605157976229828\n",
            "\n",
            "Mean absolute error: 0.32\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.66      0.68       152\n",
            "           1       0.67      0.70      0.68       148\n",
            "\n",
            "    accuracy                           0.68       300\n",
            "   macro avg       0.68      0.68      0.68       300\n",
            "weighted avg       0.68      0.68      0.68       300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 57.99999999999999 %\n",
            "\n",
            "Average precision score: 53.964089089089086 %\n",
            "\n",
            "Average recall score: 80.4054054054054 %\n",
            "\n",
            "Average F1-score: 65.38461538461539 %\n",
            "\n",
            "Matthews correlation coefficient: 0.1847236344634873\n",
            "\n",
            "Mean absolute error: 0.42\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.36      0.47       152\n",
            "           1       0.55      0.80      0.65       148\n",
            "\n",
            "    accuracy                           0.58       300\n",
            "   macro avg       0.60      0.58      0.56       300\n",
            "weighted avg       0.60      0.58      0.56       300\n",
            "\n",
            "\n",
            "[#3]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 67.33333333333333 %\n",
            "\n",
            "Average precision score: 60.863530196863536 %\n",
            "\n",
            "Average recall score: 71.62162162162163 %\n",
            "\n",
            "Average F1-score: 68.38709677419355 %\n",
            "\n",
            "Matthews correlation coefficient: 0.3488824629836733\n",
            "\n",
            "Mean absolute error: 0.32666666666666666\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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kLqqZ5VThSQYHnJnlVDbizQFnZiXISAPOAWdmaanc88FVjAPOzFLxKKqZ5ZoHGcwsn1T2KcsrxgFnZqm4i2pmueYWnJnlVjbizQFnZikJqHcLzszyKiP55oAzs7SEMtJJdcCZWWpZacFlZbTXzGpE4TYRFbW0eyypu6TbJP1F0kxJB0vqKekhSbOSnz1KrdUBZ2bpqNCCK2YpwhXA5IjYDdgbmAmMBaZExCBgSrJeEgecmaVWJxW1tEVSN+Bw4DqAiFgTEcuBkcD4ZLfxwPEl11nqL5rZlqkw4WVxSzsGAm8DN0h6VtK1kjoDfSNifrLPAqBvqbU64MwsNRX5P6C3pGmtljGtDtMA7AtcFRH7AO+xXnc0IgKIUuv0KKqZpZZiFHVxRAzbyHdzgDkR8VSyfhuFgFsoqV9EzJfUD1hUap0OuApYt66ZI0/7v/Tr041bL/83zvzujTw3800aGurZb4+PcflFJ9Ohob7aZW6xfvW9r/CZT+7J4mUrOeSkywDo3rUT1192BgP69eTN+Us5/TvX8c7K1QAcuu8gfvLNL9LQUM/S5e/yubOuqGb5NaEc98FFxAJJb0kaHBGvUHjZ/MvJMgoYl/y8q9RzVKyLKul6SYskzajUOWrV1bf8kV0HfnTZ4IQR+/P0bd/jiVsuYvUHa7npzieqWJ1NuPfPfOm8K/9u2/mjjuHRqa8w7IuX8ujUVzh/1LEAdN12G37+7RM55YJfc8iXf8xXx15XjZJrShmvwQGcC/xO0gvAUOAyCsF2jKRZwNHJekkqeQ3uRmB4BY9fk+YuXMaDj7/EaSMP+XDbsYfugVSY5nm/PT7GvEXLqlihPfHsqyxbservto341F5MuLfQU5pw71Mcd8ReAJwwfBj3/vF55iws/JstXvbu5i22FhU5glrMpJgR8VxEDIuIvSLi+IhYFhFLIuKoiBgUEUdHxNJSS61YwEXEo0DJhWXVRf95O5ecdzx1G/i/r7VN67h10tMcdfCQKlRmbenTswsLl6wAYOGSFfTp2QWAXQb0oXvXTtxz9Tf4403f4svHHVDNMmuGilyqrerX4JJRlTEA/QcMqHI1m2byYy/Su0cXhu4+gMef+es/fH/huFs5ZJ9PcMg+n6hCdZZGJON2DfV17L1bf44/+1ds3bEDD17/TabNeJ1X3yz5unfm+b2oKUREI9AIsN9+w0oeDq4FTz3/GpMfe5GHnniJDz5Yy8r33mfM98bT+MNR/PSaSSxe/i6/uehfq12mbcCipSvp26srC5esoG+vrry9bCUA8xYtZ+k777Hq/TWsen8NTzw7mz0H7bhFBxzURuusGL4ProwuPmckL933I164+1Kuu+x0Dtt/Vxp/OIqb7nyCKU/O5NoffZW6Ov+V16LJj77IyZ87EICTP3cg9z/yAgCTHnmBg4buQn19Hdt07MCwPT/OX19fUM1Sa0NG+qhVb8FtCS4Ydwv9t+/JsWf8AoB/PnIo3zpzRJWr2nJd+6Ovcuh+g+jVfVtm3PtDxjVO4vLxD3HDT87g1M8fzFsLlnL6d64H4K+vL2TKEy/z+M3fISK46a4nmPnq/HbOkH9Z6aIqojK9QkkTgCOA3sBC4OKIaHOMfb/9hsWfnppWkXqsMnrsf061S7AUPnhlIs2rFm1SOu3+T/vETXc9XNS+B+zS/Zk2bvStuIq14CLi5Eod28yqLBsNOHdRzSydwuW1bCScA87M0il+rreqc8CZWWoZyTcHnJmlJb/42czyKyP55oAzs3Rq5B7eojjgzCy9jCScA87MUvNtImaWW74GZ2b55PvgzCzP3EU1s1wSbsGZWY5lJN8ccGZWgowknAPOzFLLyoSXDjgzSy0b8eaAM7NSZCThHHBmloonvDSz/Crjjb6SXgdWAuuApogYJqkncCvwceB14MSIWFbK8f0OOzNLrcxvDTwyIoa2ejnNWGBKRAwCpiTrJXHAmVlKhQkvi1lKNBIYn3weDxxf6oEccGaWmlTcUoQAHpT0jKQxyba+EdHy8tkFQN9S6/Q1ODNLJWX3s7ek1i87boyIxlbrn4yIuZL6AA9J+kvrX46IkFTyy5sdcGaWXvEJt7itFz9HxNzk5yJJdwAHAAsl9YuI+ZL6AYtKLdNdVDNLTUX+r81jSJ0ldWn5DBwLzADuBkYlu40C7iq1TrfgzCy1Mt0m0he4IxmMaABujojJkqYCEyWNBt4ATiz1BA44M0tHUFeGgIuI14C9N7B9CXDUpp/BAWdmJfGTDGaWQ57w0sxyLSP55oAzs/TcgjOz3NqEx7A2KwecmaWWjXhzwJlZSimeM606B5yZpeYJL80sv7KRbw44M0svI/nmgDOztOTXBppZPmXpSQZPl2RmueUWnJmllpUWnAPOzFLzbSJmlk++0dfM8ipLgwwOODNLzV1UM8stt+DMLLcykm8OODMrQUYSzgFnZqkIMvOoliKi2jV8SNLbFN6DmDe9gcXVLsJSyeu/2cciYrtNOYCkyRT+foqxOCKGb8r5NkVNBVxeSZoWEcOqXYcVz/9m+eBnUc0stxxwZpZbDrjNo7HaBVhq/jfLAV+DM7PccgvOzHLLAWdmueWAqyBJwyW9Imm2pLHVrsfaJ+l6SYskzah2LbbpHHAVIqkeuBIYAQwBTpY0pLpVWRFuBKp2Y6qVlwOucg4AZkfEaxGxBrgFGFnlmqwdEfEosLTadVh5OOAqZ0fgrVbrc5JtZraZOODMLLcccJUzF+jfan2nZJuZbSYOuMqZCgySNFDSVsBJwN1Vrslsi+KAq5CIaALOAR4AZgITI+Kl6lZl7ZE0AXgSGCxpjqTR1a7JSudHtcwst9yCM7PccsCZWW454MwstxxwZpZbDjgzyy0HXIZIWifpOUkzJP1eUqdNONaNkr6UfL62rYkAJB0h6ZASzvG6pH94+9LGtq+3z7spz/UDSRemrdHyzQGXLasjYmhE7AmsAb7W+ktJJb3nNiL+NSJebmOXI4DUAWdWbQ647HoM+ETSunpM0t3Ay5LqJf1M0lRJL0g6C0AF/y+Zn+5/gD4tB5L0sKRhyefhkqZLel7SFEkfpxCk5yetx8MkbSfp9uQcUyUdmvxuL0kPSnpJ0rUU8f5zSXdKeib5nTHrfXd5sn2KpO2SbbtImpz8zmOSdivHX6blk99sn0FJS20EMDnZtC+wZ0T8LQmJdyJif0kdgT9JehDYBxhMYW66vsDLwPXrHXc74Brg8ORYPSNiqaSrgXcj4ufJfjcDl0fE45IGUHhaY3fgYuDxiLhU0meBYp4COCM5xzbAVEm3R8QSoDMwLSLOl/T95NjnUHgZzNciYpakA4H/Bj5dwl+jbQEccNmyjaTnks+PAddR6Do+HRF/S7YfC+zVcn0N6AYMAg4HJkTEOmCepP+/geMfBDzacqyI2Ni8aEcDQ6QPG2hdJW2bnONfkt+9T9KyIv5M50n6QvK5f1LrEqAZuDXZ/lvgD8k5DgF+3+rcHYs4h22hHHDZsjoihrbekPyH/l7rTcC5EfHAevsdV8Y66oCDIuL9DdRSNElHUAjLgyNilaSHga03snsk512+/t+B2cb4Glz+PAD8m6QOAJJ2ldQZeBT4cnKNrh9w5AZ+98/A4ZIGJr/bM9m+EujSar8HgXNbViS1BM6jwCnJthFAj3Zq7QYsS8JtNwotyBZ1QEsr9BQKXd8VwN8knZCcQ5L2bucctgVzwOXPtRSur01PXpzyawot9TuAWcl3N1GYMePvRMTbwBgK3cHn+aiLeA/whZZBBuA8YFgyiPEyH43mXkIhIF+i0FV9s51aJwMNkmYC4ygEbIv3gAOSP8OngUuT7V8BRif1vYSngbc2eDYRM8stt+DMLLcccGaWWw44M8stB5yZ5ZYDzsxyywFnZrnlgDOz3PpfTbhlxqCSNYMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.63      0.66       152\n",
            "           1       0.65      0.72      0.68       148\n",
            "\n",
            "    accuracy                           0.67       300\n",
            "   macro avg       0.67      0.67      0.67       300\n",
            "weighted avg       0.68      0.67      0.67       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "Precomputed matrix must be a square matrix. Input is a 700x3288 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 72.66666666666667 %\n",
            "\n",
            "Average precision score: 65.20777027027026 %\n",
            "\n",
            "Average recall score: 81.75675675675676 %\n",
            "\n",
            "Average F1-score: 74.69135802469134 %\n",
            "\n",
            "Matthews correlation coefficient: 0.4626885681782484\n",
            "\n",
            "Mean absolute error: 0.2733333333333333\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.64      0.70       152\n",
            "           1       0.69      0.82      0.75       148\n",
            "\n",
            "    accuracy                           0.73       300\n",
            "   macro avg       0.73      0.73      0.72       300\n",
            "weighted avg       0.74      0.73      0.72       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precomputed matrix must be a square matrix. Input is a 700x50 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 54.666666666666664 %\n",
            "\n",
            "Average precision score: 52.07411957411957 %\n",
            "\n",
            "Average recall score: 93.24324324324324 %\n",
            "\n",
            "Average F1-score: 66.99029126213591 %\n",
            "\n",
            "Matthews correlation coefficient: 0.1592124811828634\n",
            "\n",
            "Mean absolute error: 0.4533333333333333\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.17      0.28       152\n",
            "           1       0.52      0.93      0.67       148\n",
            "\n",
            "    accuracy                           0.55       300\n",
            "   macro avg       0.62      0.55      0.47       300\n",
            "weighted avg       0.62      0.55      0.47       300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 49.333333333333336 %\n",
            "\n",
            "Average precision score: 49.333333333333336 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 66.07142857142857 %\n",
            "\n",
            "Matthews correlation coefficient: 0.0\n",
            "\n",
            "Mean absolute error: 0.5066666666666667\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       152\n",
            "           1       0.49      1.00      0.66       148\n",
            "\n",
            "    accuracy                           0.49       300\n",
            "   macro avg       0.25      0.50      0.33       300\n",
            "weighted avg       0.24      0.49      0.33       300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 76.6 %\n",
            "\n",
            "Average precision score: 70.54959016393443 %\n",
            "\n",
            "Average recall score: 75.4 %\n",
            "\n",
            "Average F1-score: 76.31578947368422 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5321532822210989\n",
            "\n",
            "Mean absolute error: 0.234\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.78      0.77       500\n",
            "           1       0.77      0.75      0.76       500\n",
            "\n",
            "    accuracy                           0.77      1000\n",
            "   macro avg       0.77      0.77      0.77      1000\n",
            "weighted avg       0.77      0.77      0.77      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 67.7 %\n",
            "\n",
            "Average precision score: 61.38061389337641 %\n",
            "\n",
            "Average recall score: 79.60000000000001 %\n",
            "\n",
            "Average F1-score: 71.13494191242181 %\n",
            "\n",
            "Matthews correlation coefficient: 0.3644730776397911\n",
            "\n",
            "Mean absolute error: 0.323\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.56      0.63       500\n",
            "           1       0.64      0.80      0.71       500\n",
            "\n",
            "    accuracy                           0.68      1000\n",
            "   macro avg       0.69      0.68      0.67      1000\n",
            "weighted avg       0.69      0.68      0.67      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 78.60000000000001 %\n",
            "\n",
            "Average precision score: 71.81801470588233 %\n",
            "\n",
            "Average recall score: 83.0 %\n",
            "\n",
            "Average F1-score: 79.50191570881225 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5742277310441418\n",
            "\n",
            "Mean absolute error: 0.214\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.74      0.78       500\n",
            "           1       0.76      0.83      0.80       500\n",
            "\n",
            "    accuracy                           0.79      1000\n",
            "   macro avg       0.79      0.79      0.79      1000\n",
            "weighted avg       0.79      0.79      0.79      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: sigmoid\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 78.5 %\n",
            "\n",
            "Average precision score: 72.62371134020619 %\n",
            "\n",
            "Average recall score: 77.0 %\n",
            "\n",
            "Average F1-score: 78.17258883248732 %\n",
            "\n",
            "Matthews correlation coefficient: 0.5702566732674554\n",
            "\n",
            "Mean absolute error: 0.215\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.80      0.79       500\n",
            "           1       0.79      0.77      0.78       500\n",
            "\n",
            "    accuracy                           0.79      1000\n",
            "   macro avg       0.79      0.79      0.78      1000\n",
            "weighted avg       0.79      0.79      0.78      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: sigmoid\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 63.2 %\n",
            "\n",
            "Average precision score: 58.400000000000006 %\n",
            "\n",
            "Average recall score: 61.6 %\n",
            "\n",
            "Average F1-score: 62.60162601626017 %\n",
            "\n",
            "Matthews correlation coefficient: 0.26413527189768715\n",
            "\n",
            "Mean absolute error: 0.368\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.65      0.64       500\n",
            "           1       0.64      0.62      0.63       500\n",
            "\n",
            "    accuracy                           0.63      1000\n",
            "   macro avg       0.63      0.63      0.63      1000\n",
            "weighted avg       0.63      0.63      0.63      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 63.1 %\n",
            "\n",
            "Average precision score: 58.24240631163708 %\n",
            "\n",
            "Average recall score: 63.800000000000004 %\n",
            "\n",
            "Average F1-score: 63.35650446871897 %\n",
            "\n",
            "Matthews correlation coefficient: 0.2620256797749886\n",
            "\n",
            "Mean absolute error: 0.369\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.62      0.63       500\n",
            "           1       0.63      0.64      0.63       500\n",
            "\n",
            "    accuracy                           0.63      1000\n",
            "   macro avg       0.63      0.63      0.63      1000\n",
            "weighted avg       0.63      0.63      0.63      1000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_support_vector_machine_learnings(X=X, Y=Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0601682",
      "metadata": {
        "id": "d0601682"
      },
      "source": [
        "## File: 'headlines_004.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8bb71fb7",
      "metadata": {
        "id": "8bb71fb7"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2cab753c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cab753c",
        "outputId": "87a81659-bd41-4abf-9183-0c535d55cedb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LENGTH - headline: 11000; is_clickbait: 11000\n"
          ]
        }
      ],
      "source": [
        "# Reads the data from the file\n",
        "X, Y = get_json_content(i=3)\n",
        "\n",
        "# Prints the dataset size\n",
        "print(f\"LENGTH - headline: {len(X)}; is_clickbait: {len(Y)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e377e029",
      "metadata": {
        "id": "e377e029"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "199b1662",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "199b1662",
        "outputId": "a3069937-ebc7-48db-a564-5f9e4b55fcb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "Precomputed matrix must be a square matrix. Input is a 7700x4061 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 97.7878787878788 %\n",
            "\n",
            "Average precision score: 96.55440674681941 %\n",
            "\n",
            "Average recall score: 97.8125 %\n",
            "\n",
            "Average F1-score: 97.72088666874805 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9557209361481124\n",
            "\n",
            "Mean absolute error: 0.02212121212121212\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      1700\n",
            "           1       0.98      0.98      0.98      1600\n",
            "\n",
            "    accuracy                           0.98      3300\n",
            "   macro avg       0.98      0.98      0.98      3300\n",
            "weighted avg       0.98      0.98      0.98      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: rbf\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 95.06060606060606 %\n",
            "\n",
            "Average precision score: 90.75439591605219 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 95.1531370799881 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9058291831016818\n",
            "\n",
            "Mean absolute error: 0.04939393939393939\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.90      0.95      1700\n",
            "           1       0.91      1.00      0.95      1600\n",
            "\n",
            "    accuracy                           0.95      3300\n",
            "   macro avg       0.95      0.95      0.95      3300\n",
            "weighted avg       0.96      0.95      0.95      3300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "Precomputed matrix must be a square matrix. Input is a 7700x4061 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 98.57575757575758 %\n",
            "\n",
            "Average precision score: 97.58453888732527 %\n",
            "\n",
            "Average recall score: 99.0 %\n",
            "\n",
            "Average F1-score: 98.53810264385692 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9715370285690913\n",
            "\n",
            "Mean absolute error: 0.014242424242424242\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.98      0.99      1700\n",
            "           1       0.98      0.99      0.99      1600\n",
            "\n",
            "    accuracy                           0.99      3300\n",
            "   macro avg       0.99      0.99      0.99      3300\n",
            "weighted avg       0.99      0.99      0.99      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 95.69696969696969 %\n",
            "\n",
            "Average precision score: 91.84845005740529 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 95.7510472770796 %\n",
            "\n",
            "Matthews correlation coefficient: 0.917476991823829\n",
            "\n",
            "Mean absolute error: 0.04303030303030303\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.92      0.96      1700\n",
            "           1       0.92      1.00      0.96      1600\n",
            "\n",
            "    accuracy                           0.96      3300\n",
            "   macro avg       0.96      0.96      0.96      3300\n",
            "weighted avg       0.96      0.96      0.96      3300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precomputed matrix must be a square matrix. Input is a 7700x50 matrix.\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 55.96969696969697 %\n",
            "\n",
            "Average precision score: 52.70951332698714 %\n",
            "\n",
            "Average recall score: 10.5625 %\n",
            "\n",
            "Average F1-score: 18.872138470128423 %\n",
            "\n",
            "Matthews correlation coefficient: 0.19836331112187036\n",
            "\n",
            "Mean absolute error: 0.4403030303030303\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.99      0.70      1700\n",
            "           1       0.88      0.11      0.19      1600\n",
            "\n",
            "    accuracy                           0.56      3300\n",
            "   macro avg       0.71      0.55      0.44      3300\n",
            "weighted avg       0.71      0.56      0.45      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: rbf\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 48.484848484848484 %\n",
            "\n",
            "Average precision score: 48.484848484848484 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 65.3061224489796 %\n",
            "\n",
            "Matthews correlation coefficient: 0.0\n",
            "\n",
            "Mean absolute error: 0.5151515151515151\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00      1700\n",
            "           1       0.48      1.00      0.65      1600\n",
            "\n",
            "    accuracy                           0.48      3300\n",
            "   macro avg       0.24      0.50      0.33      3300\n",
            "weighted avg       0.24      0.48      0.32      3300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 98.03636363636363 %\n",
            "\n",
            "Average precision score: 96.93666769722688 %\n",
            "\n",
            "Average recall score: 98.09417040358744 %\n",
            "\n",
            "Average F1-score: 97.98432250839866 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9607034133913713\n",
            "\n",
            "Mean absolute error: 0.019636363636363636\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT4AAAEGCAYAAAD8EfnwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcvUlEQVR4nO3de7wVdb3/8dd7g4IoyFVEuXjDC2YqkqgUGRaKen7a+ZVCJhzzSHY0DfOXmv0OJ8pSq4OZt1BJyBQtL5DXkPTkLQVMTaUCU0NAcLMRQQgFPuePmY0LZe+9Rvbaa6097+d5zGPPfNesme/Czvvx/c535juKCMzM8qSm3BUwM2tpDj4zyx0Hn5nljoPPzHLHwWdmudO23BUopLbbhbbtWO5qWAYH79e33FWwDF577VVqa2u1Ncdo06lfxPq1Re0ba998MCKO2ZrzlUJlBd+2HWm3z0nlroZl8PhTV5W7CpbBkMGDtvoYsX5t0f9/+s9nr+6+1ScsgYoKPjOrBgJV91UyB5+ZZSOgpk25a7FVHHxmlp226jJh2Tn4zCwjd3XNLI/c4jOzXBFu8ZlZ3sgtPjPLIY/qmlm+eHDDzPJGuKtrZjnkFp+Z5Yu7umaWNwLaeHDDzPLG1/jMLF/c1TWzPHKLz8xyp8pbfNVdezNreVLxS5OH0quS/izpWUlz0rKukmZKmp/+7ZKWS9KVkhZIel7SwILjjEn3ny9pTFPndfCZWXY1bYpbivOZiDgoIurnxb8QmBUR/YFZ6TbACKB/uowFroUkKIHxwGDgUGB8fVg2WP0MP9XMjE2DG8UsH80JwJR0fQpwYkH51Ej8EegsqRdwNDAzIuoiYgUwE2j0BUcOPjPLrviubndJcwqWsR84UgC/kzS34LOeEbEkXX8D6Jmu7wosLPju62lZQ+UN8uCGmWWTbT6+2oIu7JZ8MiIWSdoJmCnpL4UfRkRIio9Y0wa5xWdmGTVfVzciFqV/lwF3kVyjW5p2YUn/Lkt3XwT0Kfh677SsofIGOfjMLLtmGNyQtL2kjvXrwHDgBWAGUD8yOwaYnq7PAEano7uHASvTLvGDwHBJXdJBjeFpWYPc1TWz7JrnBuaewF1KjtUWuCUiHpA0G7hd0unAa0D928vvA44FFgBrgNMAIqJO0veA2el+EyKirrETO/jMLBs1zyNrEfF34MAtlC8HjtpCeQBnNXCsycDkYs/t4DOz7PzImpnljRx8ZpYnyczzDj4zyxMJ1Tj4zCxn3OIzs9xx8JlZ7jj4zCxflC5VzMFnZpkIucVnZvlTU1Pdj/k7+MwsM7f4zCxffI3PzPLILT4zyxUPbphZLvmRNTPLF7mra2Y55OAzs9xx8JlZrnhww8zyqbpzz8FnZhnJj6yZWQ65q2tm+VPduefg+6iem/5dVq9Zx4aNG1m/fiPDxlzOBWccy+gTj2D5W6sB+N7VM5j5xEsMHNCPKy4eBST/e7n0+vu495HnNx2rpkY8PPVbLFm2kpHnXVeOn5MrZ0+4mQcfe4HuXTry5G0XA3D3Q89w2aT7+OurS5l10/kcPKDfZt9Z+EYdh5/0fS4441i+fupny1HtiuIWXyMkHQP8FGgD3BARl5byfC3tX878KXUr39ms7NpbH+aqm2dtVjbv5cV8ZvTlbNiwkZ7dOvHoLRfxwKMvsGHDRgDOHPkZ/vbKUjpu377F6p5no44/jDNO+jRnjp+6qWy/PXdh6uVnMO6Ht27xO9+ZeCefPWL/lqpiRZOqf1S3ZFcoJbUBrgZGAAOAUZIGlOp8lWztuvc2hVy7dtuQvBA+sctOnRn+yf2ZOv2JclUvd4YM3IsunTpsVrbP7jvTf7eeW9z/3keeo+8u3dh3j51bonpVoT78mloqVSmHZg4FFkTE3yPiXWAacEIJz9eiIoI7rzqbh6d+izGfH7Kp/IwvDuWxWy7iZ///FHbsuN2m8kP278cTt13M47d+m/MunbYpCH9w3v9l/JV3s3FjfOgcVn6r16zjp1NncsEZx5a7KhVFNSpqqVSlDL5dgYUF26+nZZuRNFbSHElzYv3aElaneY04YyJHnnoZXzz3Gv79C5/iiIP3ZPIdj3Lw5/+LT51yKUtr3+b73/jXTfvPffE1jjj5Eo4acznj/m047bZty9Gf/Bi1K1bx3F8WNnImK6fLJt3L10YNY4cO7cpdlYpS7S2+sg9uRMQkYBJATYedqqbZs+TNlQDUrljNPY88z8D9d+OJP7286fMpdz/ObRPP/ND3/vbqUt5Zs4799tyFwQfuwTGfOoDPHbE/7dptQ8ft2/PzCaP56n9O/dD3rDzmvPga03//LON/djcrV62lpka0a7cNY0/6dLmrVj6epKBRi4A+Bdu907Kq16H9ttTUiNVr1tGh/bYMO2xfLr/hfnp268TS5W8DcPyRBzLv5SUA9N2lG4uWrmDDho302bkL/XfbmX8sXs6Eq2cw4eoZAAwZ2J+vf/koh16Fuf/6cZvWL510L9tv1y7foUc6AXN1515Jg2820F/S7iSBNxL4UgnP12J6dOvIzZefAUCbtm2444E5zHpyHtd9dzQH7N2biOAfS+oY94NkhPDwA/fg3H8bzvr1G9i4MTj/sts+NBpsLef0i3/B43Pns/yt1ex/3He4cOyxdOm0PRf8+NfUrljNyeOu44C9d+WOn51d7qpWqMruxhZDhSOMzX5w6VjgCpLbWSZHxCWN7V/TYadot89JJauPNb8Vs68qdxUsgyGDBzF37pytSq32O+8d/cb8rKh9/3b5MXMjYlBj+6R3gMwBFkXE8WljaRrQDZgLnBoR70pqB0wFDgGWAydHxKvpMS4CTgc2AOdExIONnbOkD9xFxH0RsXdE7NlU6JlZlVDS1S1mKdK5wLyC7cuAiRGxF7CCJNBI/65Iyyem+5HeJjcS2B84BrgmDdMGVfeTxmbW4kTytFExS5PHknoDxwE3pNsChgG/SXeZApyYrp+QbpN+flS6/wnAtIhYFxGvAAtIbqdrkIPPzDLL0OLrXn+7WrqM/cChrgC+BWxMt7sBb0XE+nS78Da4TbfIpZ+vTPcv6ta5QmW/ncXMqk+GwY3ahq7xSToeWBYRcyUd2Vx1K4aDz8yyyXb9rjFDgP+TDoK2BzqRPNvfWVLbtFVXeBtc/S1yr0tqC+xIMsiR+dY5d3XNLBMhampqiloaExEXRUTviNiNZHDi9xFxCvAw8IV0tzHA9HR9RrpN+vnvI7ktZQYwUlK7dES4P/B0Y+d2i8/MMivxbXwXANMkfR/4E3BjWn4j8EtJC4A6krAkIl6UdDvwErAeOCsiNjR2AgefmWXW3DcwR8QjwCPp+t/ZwqhsRPwT+GID378EKPqWOQefmWXTfNf4ysbBZ2aZJM/qVnfyOfjMLLMqzz0Hn5llV8xTGZXMwWdm2Xg+PjPLG8/HZ2Y5VP3z8Tn4zCyzKs89B5+ZZSQPbphZzvg+PjPLJQefmeVOleeeg8/MsnOLz8zyxZMUmFneJBORVnfyOfjMLLOaKm/yOfjMLLMqzz0Hn5llI09SYGZ5VOWX+BoOPkk/A6KhzyPinJLUyMwqXmse3JjTYrUws6ohkpHdatZg8EXElMJtSR0iYk3pq2Rmla7KG3xNv1Bc0uGSXgL+km4fKOmaktfMzCqTkvn4ilkqVZPBB1wBHA0sB4iI54ChpayUmVU2qbilUhU1qhsRCz+Q3o2+pdzMWi+RjxuYF0o6AghJ2wDnAvNKWy0zq2TVPqpbTFf3TOAsYFdgMXBQum1mOVRsN7eSG4VNtvgiohY4pQXqYmZVotq7usWM6u4h6beS3pS0TNJ0SXu0ROXMrDKpyKVSFdPVvQW4HegF7AL8Gri1lJUys8qWh9tZOkTELyNifbrcDLQvdcXMrDIlo7rFLZWqweCT1FVSV+B+SRdK2k1SP0nfAu5ruSqaWUVRMhFpMUvjh1F7SU9Lek7Si5K+m5bvLukpSQsk3SZp27S8Xbq9IP18t4JjXZSW/1XS0U39hMYGN+aSTFJQX/uvFnwWwEVNHdzMWqdm6sauA4ZFxOr0VrnHJN0PnAdMjIhpkq4DTgeuTf+uiIi9JI0ELgNOljQAGAnsT3I57iFJe0dEg/cbN9jii4jdI2KP9O8HFw9umOVUc3V1I7E63dwmXQIYBvwmLZ8CnJiun5Buk35+lJIEPgGYFhHrIuIVYAFwaGPnLurJDUkfAwZQcG0vIqYW810za30ytPi6Syqc6WlSREwqOE4bkt7lXsDVwMvAWxGxPt3ldZJ7iEn/LgSIiPWSVgLd0vI/Fpyj8Dtb1GTwSRoPHEkSfPcBI4DHAAefWU5l6OjWRsSghj5Mu6MHSeoM3AXsu9WVK0Ixo7pfAI4C3oiI04ADgR1LWiszq1gStKlRUUuxIuIt4GHgcKCzpPpGWW9gUbq+COiT1EFtSXJoeWH5Fr6zRcUE39qI2Aisl9QJWPaBk5hZzjTHfXySeqQtPSRtB3yOZB6Ah0kaXABjgOnp+ox0m/Tz30dEpOUj01Hf3YH+wNONnbuYa3xz0spdT9IXXw08WcT3zKyVaqZ7k3sBU9LrfDXA7RFxTzr/5zRJ3wf+BNyY7n8j8EtJC4A6kpFcIuJFSbcDLwHrgbMaG9GF4p7V/Y909TpJDwCdIuL5zD/RzFoFoWZ5VjfNkYO3UP53tjAqGxH/BL7YwLEuAS4p9tyNvWxoYGOfRcQzxZ7EzFqRCp95pRiNtfh+0shn9ffaNKuD9+vL409d1dyHtRLqMmx8uatgGaz72+JmOU4lP4dbjMZeNvSZlqyImVUHAW1aa/CZmTWkkicgKIaDz8wyc/CZWa4k08pXd/IVMwOzJH1Z0n+m230lNfoAsJm1bq12Pr4C15A8RjIq3V5F8jCxmeVUq3/ZEDA4IgZK+hNARKyonxjQzPJHQNtKTrUiFBN876WPlAQkz9cBG0taKzOraFWee0UF35Uk08XsJOkSkoeDv1PSWplZxZKa55G1cirmWd1fSZpLMjWVgBMjYl7Ja2ZmFavKc6+oiUj7AmuA3xaWRcQ/SlkxM6tclTxiW4xiurr38v5Lh9oDuwN/JXmxh5nljCDTJKOVqJiu7gGF2+msLf/RwO5m1tpV+D16xcj85EZEPCNpcCkqY2bVQVneulGBirnGd17BZg0wEGieuW3MrOrUv16ymhXT4utYsL6e5JrfHaWpjplVg1YdfOmNyx0j4vwWqo+ZVYFqn6Sgsann26Yv7R3SkhUys8qWvF6y3LXYOo21+J4muZ73rKQZwK+Bd+o/jIg7S1w3M6tQrf7JDZJ795aTvGOj/n6+ABx8ZjnU2gc3dkpHdF/g/cCrFyWtlZlVtCpv8DUafG2AHWCLN+w4+MxyS9S04vv4lkTEhBariZlVBdG6W3xV/tPMrCQEbav8Il9jwXdUi9XCzKpGq27xRURdS1bEzKpHHm5nMTPbTJXnnoPPzLIRxb2esZI5+MwsG1V/V7fag9vMWljy5IaKWho9jtRH0sOSXpL0oqRz0/KukmZKmp/+7ZKWS9KVkhZIej6dFLn+WGPS/edLGtPUb3DwmVlmKnJpwnrgmxExADgMOEvSAOBCYFZE9AdmpdsAI4D+6TIWuBaSoATGA4OBQ4Hx9WHZEAefmWUmFbc0JiKWRMQz6foqYB6wK3ACMCXdbQpwYrp+AjA1En8EOkvqBRwNzIyIuohYAcwEjmns3L7GZ2YZKct8fN0lzSnYnhQRkz50RGk34GDgKaBnRCxJP3oD6Jmu7wosLPja62lZQ+UNcvCZWSYZR3VrI2JQo8eTdiCZ1f0bEfF2YahGREhq9rkB3NU1s8yaY3ADQNI2JKH3q4I5PpemXVjSv8vS8kVAn4Kv907LGipvuP5F/EYzs/cpmXq+mKXRwyQ73AjMi4j/LvhoBlA/MjsGmF5QPjod3T0MWJl2iR8Ehkvqkg5qDE/LGuSurpll0ow3MA8BTgX+LOnZtOzbwKXA7ZJOB14DTko/uw84FlgArAFOg+TxWknfA2an+01o6pFbB5+ZZdYcLxuKiMdo+K6XD02SEhEBnNXAsSYDk4s9t4PPzDKr7uc2HHxmlpGANlX+yJqDz8wyq/Lcc/CZWVZCVd7ZdfCZWWZu8ZlZriS3s1R38jn4zCybIiYgqHQOPjPLrNonInXwmVkmyUSk5a7F1nHwmVlmHtU1s9yp8p6ug685nD3hZh587AW6d+nIk7ddDMCKle/wlW9P5h9L6ujbqyu/+OHpdO7Ugdvvn81Pp84kItihQ3t+cuHJHLB37zL/gnx47pZvsHrNu2zYuJH1GzYy7GuTmPDV4Rx9+N68994GXlmygrMuu5u33/knfXp25qmbzmbBwloA5rz0OuddcQ8AB/bvxTUXfJ727doy86n5XHjV/eX8WWVR7S2+kk1LJWmypGWSXijVOSrFqOMP4zdXbv7s9MQpMxn6iX2Ye+d4hn5iHyZO+R0A/Xbpxr0//wZPTLuY/3f6MYz7wa3lqHJu/ct5NzF07HUM+1oyCfDDc1/miK9cwyfPuJaXFy7nvC99atO+ry6uY+jY6xg69rpNoQfwk3HHc+5PZnDIqVey567d+Oyhe7X47yin+mt8xSyVqpTz8d1EE/PetxZDBu5Fl04dNiu7/3+eZ9TxgwEYdfxg7nvkeQAGH7gHndN9P3HA7ixe9lbLVtY28/Ccl9mwcSMAs+ctZJcenRrdv2fXHejYoR1z5r0OwLSZz3LckP1KXs+KUuQkpJU88luy4IuIPwCNzonVmi2rW8XO3XcEoGe3TiyrW/WhfX45/Qk+e8SAlq5abkXAnT86lYev+ypjjjvkQ59/ecRAHnp6/qbtvjt34X9+fib3TDyNww/oC0Cv7p1Y/Obbm/ZZ/Obb9OresfSVrzDN9Ja1sin7NT5JY0leFUefvn3LXJvSSGaj3bzs0Tl/4+YZT3L/9ePKU6kcGnHujSypXUX3zttz149GM39hLU88/xoA3zxlKOs3bOT2h5KW+dK6VRww6r9Z8fZaDuzfi199bxSHf+Xqcla/YtS/V7ealX3q+YiYFBGDImJQj+49yl2dZrNT1468UbsSgDdqV9Kjy/utghfmL+Kc79/Cr348lq6ddyhXFXNnSW3S6q596x3ueWweA/dNXsQ16uiDGH7Y3oy95I5N+7773gZWvL0WgOfmL+GVxXXs2bsbS2rf3qw7vEuPTpuOmyfV3uIre/C1VscMPYBb73kKgFvveYoRn/44AAvfqGP0t67nuu+OZq9+PRs7hDWjDu23YYfttt20PmzQnsx7ZRlHfWIvzjl5CF/6zi2sXffepv277diBmvTqfL9eXdijdzdeXbKCpXWrWbVmHYP2S0biR37uIO574i8t/4PKrcqTr+xd3dbg9It/weNz57P8rdXsf9x3uHDssYwb8zlOu2gyN894kj47d+UXP/wKAD+64X7qVr7D+ZfdBkDbtjU8PPWCclY/F3p02YGbJ4wEoE2bGu6Y9WdmzV7A3F+eQ7tt2nLXj0YD79+2csTH+3HRacNYv34DGyP45sTf8taqpAV4/hX3cs0FJ9K+3TY89PR8Zj41v8HztlbV3tVVMo19CQ4s3QocCXQHlgLjI+LGxr5zyCGD4vGn5jS2i1WYLsPGl7sKlsG6P13PxlWLtyq19jvg4Jg6/ZGi9j10z85zm3qvbjmUrMUXEaNKdWwzK7PqbvC5q2tm2SSX76o7+Rx8ZpaN5+Mzszyq8txz8JlZVmqWF4qXk4PPzDKr8txz8JlZNhV+b3JRHHxmll2VJ5+Dz8wy8+0sZpY7vsZnZvnSCu7j8+wsZpaZivy/Jo+zhVdUSOoqaaak+enfLmm5JF0paYGk5yUNLPjOmHT/+ZLGNHVeB5+ZZSKSFl8xSxFu4sOvqLgQmBUR/YFZ6TbACKB/uowFroUkKIHxwGDgUGB8fVg2xMFnZpk113R8Dbyi4gRgSro+BTixoHxqJP4IdJbUCzgamBkRdRGxAphJE+/78TU+M8uu+Gt83SUVzjU3KSImNfGdnhGxJF1/A6ifsXdXYGHBfq+nZQ2VN8jBZ2aZZZiItHZr5uOLiJDU7JOGuqtrZpmVeOb5pWkXlvTvsrR8EdCnYL/eaVlD5Q1y8JlZdqVNvhlA/cjsGGB6QfnodHT3MGBl2iV+EBguqUs6qDE8LWuQu7pmlklzTkRa+IoKSa+TjM5eCtwu6XTgNeCkdPf7gGOBBcAa4DSAiKiT9D1gdrrfhIho9J3eDj4zy6YZb2Bu5BUVR21h3wDOauA4k4HJxZ7XwWdmmVX5gxsOPjPLyhORmlkOVXnuOfjMLBtPRGpm+VTlyefgM7PMPBGpmeWOr/GZWb4Iahx8ZpY/1Z18Dj4zy6R+ItJq5uAzs8yqPPccfGaWnVt8ZpY7fmTNzHKnumPPwWdmGWV4g1rFcvCZWWZ+csPM8qe6c8/BZ2bZVXnuOfjMLCtleb1kRXLwmVkmreHJDb9e0sxyxy0+M8us2lt8Dj4zy8y3s5hZvvgGZjPLm9YwuOHgM7PM3NU1s9xxi8/McqfKc8/BZ2YfQZUnn4PPzDIRVP0ja4qIctdhE0lvAq+Vux4l0B2oLXclLJPW+t+sX0T02JoDSHqA5N+nGLURcczWnK8UKir4WitJcyJiULnrYcXzf7PWzc/qmlnuOPjMLHccfC1jUrkrYJn5v1kr5mt8ZpY7bvGZWe44+Mwsdxx8JSTpGEl/lbRA0oXlro81TdJkScskvVDuuljpOPhKRFIb4GpgBDAAGCVpQHlrZUW4Cai4G26teTn4SudQYEFE/D0i3gWmASeUuU7WhIj4A1BX7npYaTn4SmdXYGHB9utpmZmVmYPPzHLHwVc6i4A+Bdu90zIzKzMHX+nMBvpL2l3StsBIYEaZ62RmOPhKJiLWA2cDDwLzgNsj4sXy1sqaIulW4ElgH0mvSzq93HWy5udH1swsd9ziM7PccfCZWe44+Mwsdxx8ZpY7Dj4zyx0HXxWRtEHSs5JekPRrSR224lg3SfpCun5DYxMoSDpS0hEf4RyvSvrQ27gaKv/APqsznuu/JJ2ftY6WTw6+6rI2Ig6KiI8B7wJnFn4o6SO9Jzki/j0iXmpklyOBzMFnVqkcfNXrUWCvtDX2qKQZwEuS2kj6kaTZkp6X9FUAJa5K5wd8CNip/kCSHpE0KF0/RtIzkp6TNEvSbiQBOy5tbX5KUg9Jd6TnmC1pSPrdbpJ+J+lFSTeQvHu6UZLuljQ3/c7YD3w2MS2fJalHWranpAfS7zwqad/m+Me0fPlILQQrr7RlNwJ4IC0aCHwsIl5Jw2NlRHxCUjvgcUm/Aw4G9iGZG7An8BIw+QPH7QFcDwxNj9U1IuokXQesjogfp/vdAkyMiMck9SV5OmU/YDzwWERMkHQcUMxTD19Jz7EdMFvSHRGxHNgemBMR4yT9Z3rss0leAnRmRMyXNBi4Bhj2Ef4ZLcccfNVlO0nPpuuPAjeSdEGfjohX0vLhwMfrr98BOwL9gaHArRGxAVgs6fdbOP5hwB/qjxURDc1L91lggLSpQddJ0g7pOf41/e69klYU8ZvOkfT5dL1PWtflwEbgtrT8ZuDO9BxHAL8uOHe7Is5hthkHX3VZGxEHFRakAfBOYRHw9Yh48AP7HduM9agBDouIf26hLkWTdCRJiB4eEWskPQK0b2D3SM/71gf/Dcyy8jW+1udB4GuStgGQtLek7YE/ACen1wB7AZ/Zwnf/CAyVtHv63a5p+SqgY8F+vwO+Xr8hqT6I/gB8KS0bAXRpoq47AivS0NuXpMVZrwaob7V+iaQL/TbwiqQvpueQpAObOIfZhzj4Wp8bSK7fPZO+MOfnJC37u4D56WdTSWYg2UxEvAmMJelWPsf7Xc3fAp+vH9wAzgEGpYMnL/H+6PJ3SYLzRZIu7z+aqOsDQFtJ84BLSYK33jvAoelvGAZMSMtPAU5P6/cins7fPgLPzmJmueMWn5nljoPPzHLHwWdmuePgM7PccfCZWe44+Mwsdxx8ZpY7/wt/4rp3cxVccgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      5648\n",
            "           1       0.98      0.98      0.98      5352\n",
            "\n",
            "    accuracy                           0.98     11000\n",
            "   macro avg       0.98      0.98      0.98     11000\n",
            "weighted avg       0.98      0.98      0.98     11000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: rbf\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 95.2090909090909 %\n",
            "\n",
            "Average precision score: 91.03589045756081 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 95.30763066512331 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9085239202055382\n",
            "\n",
            "Mean absolute error: 0.04790909090909091\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT4AAAEGCAYAAAD8EfnwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcZUlEQVR4nO3debxVZb3H8c/3HBBwYhAkRFI0UtEckNC0TNFU1MS6VpolGkl6tbqWmlZmat3UbtnkREai5qxcxzRCuQ5pCoqkIEGKAoKMkogT8Lt/7OfgBjnn7AVnn733Wd93r/VirWdNz4aX3561nrWepYjAzCxP6ipdATOz1ubgM7PccfCZWe44+Mwsdxx8ZpY77SpdgWJqv3GoQ+dKV8My2KXfVpWugmUw+5WXWbxooTbkGPWbbxOx4q2Sto23FjwQEYduyPnKobqCr0NnOuw+vNLVsAzuu/vcSlfBMjhs8D4bfIxY8RYddvhiSdu+Pemy7ht8wjKoquAzs1ogUG3fJXPwmVk2AurqK12LDeLgM7PstEG3CSvOwWdmGflS18zyyC0+M8sV4RafmeWN3OIzsxxyr66Z5Ys7N8wsb0TNX+rWdmybWWWorrSpucNIMyX9Q9IkSRNSWTdJYyVNT392TeWS9BtJMyRNljSg6DjD0vbTJQ1r7rwOPjPLSC0WfMkBEbF7RAxMy2cD4yKiHzAuLQMMAfqlaQRwBRSCEjgP2AsYBJzXEJaNcfCZWTYC6utLm9bPUGB0mh8NHFVUfm0UPAF0kdQLOAQYGxGLI2IJMBZockQYB5+ZZSeVNkF3SROKphFrHSmAv0iaWLSuZ0TMTfPzgJ5pvjcwq2jf2amssfJGuXPDzDLK1Ku7sOgSdl0+GRFzJG0JjJX0QvHKiAhJLf4pSLf4zCy70lt8TYqIOenP+cAYCvfoXkuXsKQ/56fN5wB9inbfOpU1Vt4oB5+ZZdcCnRuSNpG0WcM8cDDwHHAX0NAzOwy4M83fBRyfenf3BpamS+IHgIMldU2dGgenskb5UtfMsimxNVeCnsAYFY7VDrghIu6X9BRwi6ThwMtAw3DP9wGHATOA5cCJABGxWNKFwFNpuwsiYnFTJ3bwmVl2LfDKWkS8COy2jvJFwIHrKA/g1EaONQoYVeq5HXxmlpFfWTOzPKrxV9YcfGaWjcfjM7P88aWumeWRx+Mzs9zxPT4zyxX5UtfM8sgtPjPLGzn4zCxPCiPPO/jMLE8kVOfgM7OccYvPzHLHwWdmuePgM7N8UZpqmIPPzDIRcovPzPKnrs5vbphZzrjFZ2b54nt8ZpZHbvGZWa64c8PMcsmvrJlZvsiXumaWQw4+M8sdB5+Z5Yo7N8wsn2o79xx8ZpaR/MqameWQL3XNLH9qO/ccfOvr2Zu+y7Ll77ByVbBi5SoGf+MKhn56Z753wmB22KYHB55yJZOmvQrA/ntuz3kjDmaj9vW8+95KfnTlAzzyzIsA/HD4QRxzyB503qwjfYZcWMmflCv7H/sTNtm4A3V1dbSrr2PMladz0ZV389Djz9O+fTs+3GsLLvreMWy+aSfu/OtErr55/Op9p704l/+96nT6f6R35X5AhbnF1wRJhwK/BuqBqyPionKer7V99vRRLF66fPXy1Jfmc/yPbuTS7w5dY7tFS5dz7PevZ96iN9ip75bcdskJ7PyFSwC4//EX+P2YJ5jwp9Nbte4G1/3yFLp13nT18r57fpQzTjqMdvX1XDLyHq68YRxnjTiCoQftydCD9gQKoXfKuX/Mfei1ZPBJqgcmAHMi4ghJfYGbgC2AicBXI+JdSR2Aa4E9gUXAlyJiZjrGOcBwYCXwrYh4oKlzlu0OZfoxlwFDgP7AsZL6l+t81eCfryxgxqyFHyj/x4y5zFv0BlAIx04d2rFR+3oAJkyZzWuLl7VqPW3dPvXxHWhXX/h32X2nbZi34PUPbHPPg89wxODdW7tqVach/JqbSvRtYGrR8sXApRHxEWAJhUAj/bkklV+atiPlyjHAzsChwOUpfxpVzq6ZQcCMiHgxIt6lkOBDm9mnZkTAHT8/gYeuOoVhRwwseb8jP70zz06fy7vvrSxj7aw5kjjxzJEc9Y1Luemexz+w/rY/P8mnB+30gfJ7H5rEEYP3aI0qVjXVqaSp2eNIWwOHA1enZQGDgdvSJqOBo9L80LRMWn9g2n4ocFNEvBMRLwEzKORPo8p5qdsbmFW0PBvYa+2NJI0ARgDQYfMyVqdlDfnmSOYufIPuXTZhzP+cwPRXFvK3yTOb3GfHbbfkxyMO4fNnXtMqdbTG3fjr0/hQj84sWvIGJ5x5Fdv12ZJBu20PwOXX/5V29XUcedCANfaZNPVlOnVsz0f79qpElatKhtZcd0kTipZHRsTIouVfAWcBm6XlLYDXI2JFWp5NIUugKFMiYoWkpWn73sATRccs3medKv4wTkSMjIiBETFQ7TaudHVKNndh4dJ14etvcs+jUxmwU9P3fLbqsTnXXfhlTvnZbcx8dXFrVNGa8KEenQHYoutmfOaTH2PyC68AcPv9T/LQE1P4xQ+O+8B/3Pc+6NYesHqQghIvdRc2/PedptWhJ+kIYH5ETGztn1DO4JsD9Cla3jqV1byNO7Zn004brZ4fPPAjTH1pfqPbb75pR27+2Vc5f+Rf+Ptzr7RWNa0Ry996h2XL3149/+iEaXy0by8efvIFfn/zeK78ydfo1HGjNfZZtWoVfx4/icMPcPAJkEqbmrEvcKSkmRRuhQ2m0BnaRVLD1WhxbqzOlLS+M4VOjsxZU85L3aeAfqmHZg6Fm49fLuP5Wk2Prpty/YWFn1JfX8ft4yYz7snpHP7Jnbj420fQvfMm3Pyz4/nHjLkcfdZoTvrc3vTtvQVnDTuAs4YdAMDnz7iGha+/yfnfOIT/OGhXNu7QnuduPZPr7p3Ixdc8WMmf1+YtXLKMU3/0RwBWrFzFZw8cwH6DduTAr/w37763ghPOvAqA3ftvw4WnHw3AU5Nf5ENbduHDW21RsXpXj5bp1Y2Ic4BzACTtD5wREcdJuhU4mkIYDgPuTLvclZYfT+sfjIiQdBdwg6RfAlsB/YAnm/wFEbHBP6DRg0uHUbiGrwdGRcRPm9q+btNe0WH34U1tYlVm+t3nVroKlsFhg/dh8jMTNyi1On7oo7HNsN+WtO0/Lzl0YkQ02/tXFHxHSNqOQuh1A54BvhIR70jqCFwH7AEsBo6JiBfT/j8AvgasAP4rIv7c1PnK+hxfRNwH3FfOc5hZKyvtMjaTiBgPjE/zL7KOXtmIeBv4QiP7/xRosmFVzG9umFkmAuo89LyZ5U2Nv7Hm4DOz7PyurpnlSxnu8bU2B5+ZZSLkgUjNLH/c4jOz3PE9PjPLF9/jM7O8KbyrW9vJ5+Azs8xqPPccfGaWnd/cMLN8kS91zSxnGsbjq2UOPjPLqGW/slYJDj4zy6zGc8/BZ2YZyZ0bZpYzfo7PzHLJwWdmuVPjuefgM7Ps3OIzs3zxIAVmljeFgUhrO/kcfGaWWV2NN/kcfGaWWY3nnoPPzLKRBykwszyq8Vt8jQefpN8C0dj6iPhWWWpkZlWvLXduTGi1WphZzRCFnt1a1mjwRcTo4mVJG0fE8vJXycyqXY03+Gj2q8CSPiFpCvBCWt5N0uVlr5mZVScVxuMrZapWpXwO/VfAIcAigIh4FtivnJUys+omlTZVq5J6dSNi1lrpvbI81TGzaidq/wHmUlp8syTtA4Sk9pLOAKaWuV5mVsXq6lTS1BRJHSU9KelZSc9LOj+V95X0d0kzJN0saaNU3iEtz0jrty061jmpfJqkQ5qtfwm/8WTgVKA38Cqwe1o2sxwq9TK3hEbhO8DgiNiNQq4cKmlv4GLg0oj4CLAEGJ62Hw4sSeWXpu2Q1B84BtgZOBS4XFJ9UyduNvgiYmFEHBcRPSOiR0R8JSIWNfuTzKzNqpNKmpoSBcvSYvs0BTAYuC2VjwaOSvND0zJp/YEq3IMbCtwUEe9ExEvADGBQk/Vv7gdK2k7S3ZIWSJov6U5J2zW3n5m1XSpxArpLmlA0jVjjOFK9pEnAfGAs8C/g9YhYkTaZTeFqk/TnLIC0fimwRXH5OvZZp1I6N24ALgM+l5aPAW4E9iphXzNrgzI8qrIwIgY2tjIiVgK7S+oCjAF2bIHqNauUe3wbR8R1EbEiTdcDHctdMTOrToVe3dKmUkXE68BDwCeALpIaGmVbA3PS/BygD0Ba35nCY3ary9exzzo1GnySuknqBvxZ0tmStpW0jaSzgPtK/0lm1qaotB7dEnp1e6SWHpI6AZ+h8MTIQ8DRabNhwJ1p/q60TFr/YEREKj8m9fr2BfoBTzZ17qYudSdSuNHYUPtvFK0L4Jwmf5WZtVkt9FZGL2B06oGtA26JiHvSm2I3SfoJ8Azwh7T9H4DrJM0AFlO47UZEPC/pFmAKsAI4NV1CN6qpd3X7buCPMrM2qOFSd0NFxGRgj3WUv8g6emUj4m3gC40c66fAT0s9d0lvbkjaBehP0b29iLi21JOYWdtSze/hlqLZ4JN0HrA/heC7DxgCPAo4+MxyqrZjr7Re3aOBA4F5EXEisBuF3hQzyyEJ6utU0lStSrnUfSsiVklaIWlzCg8a9mluJzNru9r8pS4wIXU5/55CT+8y4PGy1srMqlqN517zwRcR/5lmr5R0P7B56o0xsxwSzb+HW+2a+tjQgKbWRcTT5amSmVW1Kh9ktBRNtfh+0cS6hhEUWtQeO/TmsfE/aenDWhl1/fhpla6CZfDOtFnNb1SCNnuPLyIOaM2KmFltEFDfVoPPzKwxVfykSkkcfGaWmYPPzHKlMKx8bSdfKSMwS9JXJP0oLX9YUpPDOptZ29bS4/G1tlJeWbucwuCAx6blNyiMyGxmOZWH7+ruFREDJD0DEBFLGj73Zmb5I6BdNadaCUoJvvfSQIEBhVFTgVVlrZWZVbUaz72Sgu83FD4CsqWkn1IYreWHZa2VmVUtlfDpyGpXyru6f5I0kcLQVAKOioipZa+ZmVWtGs+9kgYi/TCwHLi7uCwiXilnxcyselVzj20pSrnUvZf3PzrUEegLTAN2LmO9zKxKCap6kNFSlHKp+7Hi5TRqy382srmZtXVV/oxeKTK/uRERT0vaqxyVMbPaoBr/6kYp9/i+U7RYBwwAXi1bjcysqrXU5yUrqZQW32ZF8yso3PO7vTzVMbNa0KaDLz24vFlEnNFK9TGzGlDrgxQ0NfR8u4hYIWnf1qyQmVW3wuclK12LDdNUi+9JCvfzJkm6C7gVeLNhZUTcUea6mVmVavNvblB4dm8RhW9sNDzPF4CDzyyH2nrnxpapR/c53g+8BlHWWplZVavxBl+TwVcPbArrfGDHwWeWW6KuDT/HNzciLmi1mphZTRC13+Jrqm+mxn+amZWFoF2dSpqaPIzUR9JDkqZIel7St1N5N0ljJU1Pf3ZN5ZL0G0kzJE1Or882HGtY2n66pGHN/YSmgu/A0v4WzCxPGlp8LTD0/ArguxHRH9gbOFVSf+BsYFxE9APGpWWAIUC/NI0AroBCUALnAXsBg4DzGsKyMY0GX0QsbrbaZpZLdWkw0uampkTE3Ih4Os2/AUwFegNDgdFps9HAUWl+KHBtFDwBdJHUCzgEGBsRiyNiCTAWOLSpc/vzkmaWWUvf45O0LbAH8HegZ0TMTavmAT3TfG9gVtFus1NZY+WNcvCZWSaitM8zJt0lTShaHhkRI9c4nrQphff//ysi/l38OlxEhKQWf4rEwWdm2SjTmxsLI2Jgo4eS2lMIvT8VvQ32mqReETE3XcrOT+VzgD5Fu2+dyuYA+69VPr6pStX4G3dm1toKb25s+D0+FZp2fwCmRsQvi1bdBTT0zA4D7iwqPz717u4NLE2XxA8AB0vqmjo1Dk5ljXKLz8wya6FbfPsCXwX+IWlSKvs+cBFwi6ThwMvAF9O6+4DDgBkUvgN0IhQ6YiVdCDyVtruguc5ZB5+ZZdYSnRsR8SiNZ+gHHqeLiABObeRYo4BRpZ7bwWdmGantjsdnZrYuGXt1q5KDz8wyy8N4fGZm71MbHnrezGxdfKlrZrnkFp+Z5U5tx56Dz8wyElDvFp+Z5U2N556Dz8yyEqrxi10Hn5ll5hafmeVK4XGW2k4+B5+ZZVPa9zSqmoPPzDLzK2tmliuFgUgrXYsN4+Azs8zcq2tmuVPjV7oOvnL669+mcM4vbmPlqlV8deg+nH7CwZWuUq49e+f5LFv+DitXrWLFilUMHnYJ3z/5cA7bb1dWRbBg8Rucev71zFu4lH0H9OOGX4zg5VcXAXD3Q5P4+dX307tnF6748fH06LYZAYwe8xhX3TS+or+rEtzia4SkUcARwPyI2KVc56lWK1eu4sxLbmHM705jq55dGDzs5wzZ72PsuF2vSlct1z578q9ZvPTN1cu/vW4c/33lvQCM+NKnOevrQ/jORTcB8Pgz/+KY71y5xv4rVqzih7+6g8nTZrPpxh146NrvMf7vLzDtpXmt9yMqrC3c4yvn6DLX0MzXzNuyic/PZLs+3dl26+5s1L4dn//MAO77v8mVrpat5Y033149v0mnDhQ+69C41xb9m8nTZgOwbPk7/HPmPHr16FLWOladEr+wVs09v2Vr8UXEw+nr6Lk0d8FSevfsunp5q55dmfjczMpVyIgI7vjdaUQE14x5jNFjHgPgh6d8lmMOH8S/l73FZ0/+zertP/6xvjzyp7OZt3Ap5/56DC+8uGarrk+vbuy6w9ZMfH5ma/6MqlC9kVaaio8nKGmEpAmSJixYuKDS1bE2bMhJl7L/Vy/mC9++nK8f/Sn22WN7AH5yxd3scsS53Hr/BE764n4ATJ42i12PPJdPHXcRI2/+P67/+Yg1jrVJp4249uKvc84vb1+j1ZgHLfVd3UqqePBFxMiIGBgRA3t071Hp6rSYXj06M+e1JauXX31tCb16dK5gjWzugqUALFyyjHvGT2bAztuusf7WPz/FkYN3BwqXwG++9S4AY/82hfbt6unWeRMA2tXXMfrik7j1/gnc89CzrfcDqohKnKpVxYOvrRrQfxv+9coCXp6zkHffW8EdY59myH67VrpaubVxx43YdOMOq+cH770jU//1Ktv1ef//bId8elf+OfM1ALbcYrPV5QP6b0NdnVZ3ivz23OP458x5XH7Dg634C6pMjSefH2cpk3bt6rnkrC/yH9+6jJUrg+OO3JudtnePbqX02GIzrr/kJADq29Vz+/0TGPf4VEZf/HX6bbMlq1YFs+Yt5js/K/ToDh28Byce/SlWrljJW++8x/Af/BGAvXfbjmMO34vnp8/h4T+dDcCFl93F2L9NqcwPq5BqvowthZrrxVrvA0s3AvsD3YHXgPMi4g9N7bPnngPjsb9PKEt9rDy6fvy0SlfBMnhn2i2sWj5/g1Jrp4/tEdfeOb6kbQdt32ViRAzckPOVQzl7dY8t17HNrMJqu8HnS10zy6Zw+662k8/BZ2bZeDw+M8ujGs89B5+ZZSV/UNzM8qfGc88PMJtZNqU+u1xKNkoaJWm+pOeKyrpJGitpevqzayqXpN9ImiFpsqQBRfsMS9tPlzSsufM6+Mwsu5Z7c+MaPjiK09nAuIjoB4xLywBDgH5pGgFcAYWgBM4D9gIGAec1hGVjHHxmlplK/F9zIuJhYPFaxUOB0Wl+NHBUUfm1UfAE0EVSL+AQYGxELI6IJcBYmhkSz/f4zCyzDPf4uksqfh1rZESMbGafnhExN83PA3qm+d7ArKLtZqeyxsob5eAzs2yyPce3cENeWYuIkNTi79X6UtfMMmupS91GvJYuYUl/zk/lc4A+RdttncoaK2+Ug8/MMhGFFl8p03q6C2jomR0G3FlUfnzq3d0bWJouiR8ADpbUNXVqHJzKGuVLXTPLrKUe4ysexUnSbAq9sxcBt0gaDrwMfDFtfh9wGDADWA6cCBARiyVdCDyVtrsgItbuMFmDg8/Msmuh5GtiFKcD17FtAKc2cpxRwKhSz+vgM7PMan0gUgefmWVW27Hn4DOz9VHjyefgM7NMPBCpmeWPByI1szyq8dxz8JlZVh6I1MxyqMZzz8FnZtmUPtRe9XLwmVl2NZ58Dj4zy8yPs5hZ7vgen5nli6DOwWdm+VPbyefgM7NMGgYirWUOPjPLrMZzz8FnZtm5xWdmueNX1swsd2o79hx8ZpbRBn5BrSo4+MwsM7+5YWb5U9u55+Azs+xqPPccfGaWlfx5STPLl7bw5kZdpStgZtba3OIzs8xqvcXn4DOzzPw4i5nlix9gNrO8aQudGw4+M8vMl7pmlju13uLz4yxmlplKnJo9jnSopGmSZkg6u1z1XZuDz8yya4Hkk1QPXAYMAfoDx0rqX7Y6F3HwmVkmAuqkkqZmDAJmRMSLEfEucBMwtNz1hyq7x/f00xMXdmqvlytdjzLoDiysdCUsk7b6b7bNhh7g6acnPtCpvbqXuHlHSROKlkdGxMg03xuYVbRuNrDXhtavFFUVfBHRo9J1KAdJEyJiYKXrYaXzv1njIuLQStdhQ/lS18wqZQ7Qp2h561RWdg4+M6uUp4B+kvpK2gg4BrirNU5cVZe6bdjI5jexKuN/szKLiBWSTgMeAOqBURHxfGucWxHRGucxM6savtQ1s9xx8JlZ7jj4yqhSr+PY+pM0StJ8Sc9Vui5WPg6+Mqnk6zi2Qa4Bav45NWuag698KvY6jq2/iHgYWFzpelh5OfjKZ12v4/SuUF3MrIiDz8xyx8FXPhV7HcfMmubgK5+KvY5jZk1z8JVJRKwAGl7HmQrc0lqv49j6k3Qj8Diwg6TZkoZXuk7W8vzKmpnljlt8ZpY7Dj4zyx0Hn5nljoPPzHLHwWdmuePgqyGSVkqaJOk5SbdK2ngDjnWNpKPT/NVNDaAgaX9J+6zHOWZKH/waV2Pla22zLOO5fizpjKx1tHxy8NWWtyJi94jYBXgXOLl4paT1+pRARHw9IqY0scn+QObgM6tWDr7a9QjwkdQae0TSXcAUSfWSfi7pKUmTJX0DQAW/S+MD/hXYsuFAksZLGpjmD5X0tKRnJY2TtC2FgD09tTY/JamHpNvTOZ6StG/adwtJf5H0vKSrKXx7ukmS/lfSxLTPiLXWXZrKx0nqkcq2l3R/2ucRSTu2xF+m5Ys/NlSDUstuCHB/KhoA7BIRL6XwWBoRH5fUAXhM0l+APYAdKIwN2BOYAoxa67g9gN8D+6VjdYuIxZKuBJZFxP+k7W4ALo2IRyV9mMLbKTsB5wGPRsQFkg4HSnnr4WvpHJ2ApyTdHhGLgE2ACRFxuqQfpWOfRuEjQCdHxHRJewGXA4PX46/RcszBV1s6SZqU5h8B/kDhEvTJiHgplR8M7Npw/w7oDPQD9gNujIiVwKuSHlzH8fcGHm44VkQ0Ni7dQUB/aXWDbnNJm6ZzfD7te6+kJSX8pm9J+lya75PqughYBdycyq8H7kjn2Ae4tejcHUo4h9kaHHy15a2I2L24IAXAm8VFwDcj4oG1tjusBetRB+wdEW+voy4lk7Q/hRD9REQslzQe6NjI5pHO+/rafwdmWfkeX9vzAHCKpPYAkj4qaRPgYeBL6R5gL+CAdez7BLCfpL5p326p/A1gs6Lt/gJ8s2FBUkMQPQx8OZUNAbo2U9fOwJIUejtSaHE2qAMaWq1fpnAJ/W/gJUlfSOeQpN2aOYfZBzj42p6rKdy/ezp9MOcqCi37McD0tO5aCiOQrCEiFgAjKFxWPsv7l5p3A59r6NwAvgUMTJ0nU3i/d/l8CsH5PIVL3leaqev9QDtJU4GLKARvgzeBQek3DAYuSOXHAcNT/Z7Hw/nbevDoLGaWO27xmVnuOPjMLHccfGaWOw4+M8sdB5+Z5Y6Dz8xyx8FnZrnz/x7boAiYX11JAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95      5648\n",
            "           1       0.91      1.00      0.95      5352\n",
            "\n",
            "    accuracy                           0.95     11000\n",
            "   macro avg       0.96      0.95      0.95     11000\n",
            "weighted avg       0.96      0.95      0.95     11000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: linear\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 98.37272727272726 %\n",
            "\n",
            "Average precision score: 97.3832166335839 %\n",
            "\n",
            "Average recall score: 98.57997010463379 %\n",
            "\n",
            "Average F1-score: 98.33193551393158 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9674469383534758\n",
            "\n",
            "Mean absolute error: 0.016272727272727272\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.98      0.98      5648\n",
            "           1       0.98      0.99      0.98      5352\n",
            "\n",
            "    accuracy                           0.98     11000\n",
            "   macro avg       0.98      0.98      0.98     11000\n",
            "weighted avg       0.98      0.98      0.98     11000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 95.82727272727273 %\n",
            "\n",
            "Average precision score: 92.10118740320083 %\n",
            "\n",
            "Average recall score: 100.0 %\n",
            "\n",
            "Average F1-score: 95.88820209621069 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9198713781825422\n",
            "\n",
            "Mean absolute error: 0.041727272727272724\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.92      0.96      5648\n",
            "           1       0.92      1.00      0.96      5352\n",
            "\n",
            "    accuracy                           0.96     11000\n",
            "   macro avg       0.96      0.96      0.96     11000\n",
            "weighted avg       0.96      0.96      0.96     11000\n",
            "\n",
            "\n",
            "[#3]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: rbf\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 98.36363636363636 %\n",
            "\n",
            "Average precision score: 96.95399699260948 %\n",
            "\n",
            "Average recall score: 99.51420029895365 %\n",
            "\n",
            "Average F1-score: 98.33825701624815 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9675316285023481\n",
            "\n",
            "Mean absolute error: 0.016363636363636365\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.98      5648\n",
            "           1       0.97      1.00      0.98      5352\n",
            "\n",
            "    accuracy                           0.98     11000\n",
            "   macro avg       0.98      0.98      0.98     11000\n",
            "weighted avg       0.98      0.98      0.98     11000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n",
            "X should be a square kernel matrix\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: C-Support Vector Classification\n",
            "- C: 1.0\n",
            "- break_ties: False\n",
            "- cache_size: 200\n",
            "- class_weight: None\n",
            "- coef0: 0.0\n",
            "- decision_function_shape: ovr\n",
            "- degree: 3\n",
            "- gamma: scale\n",
            "- kernel: poly\n",
            "- max_iter: -1\n",
            "- probability: False\n",
            "- random_state: None\n",
            "- shrinking: True\n",
            "- tol: 0.001\n",
            "- verbose: False\n",
            "\n",
            "Accuracy: 95.24545454545455 %\n",
            "\n",
            "Average precision score: 93.03335654529515 %\n",
            "\n",
            "Average recall score: 94.63751868460389 %\n",
            "\n",
            "Average F1-score: 95.09058481179011 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9048553962903438\n",
            "\n",
            "Mean absolute error: 0.047545454545454544\n",
            "\n",
            "Confusion matrix:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.96      0.95      5648\n",
            "           1       0.96      0.95      0.95      5352\n",
            "\n",
            "    accuracy                           0.95     11000\n",
            "   macro avg       0.95      0.95      0.95     11000\n",
            "weighted avg       0.95      0.95      0.95     11000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_support_vector_machine_learnings(X=X, Y=Y)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "Support_Vector_Machines",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
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