Random_Forest.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5733cb4b",
      "metadata": {
        "id": "5733cb4b"
      },
      "outputs": [],
      "source": [
        "from utils.utils import get_json_content\n",
        "from utils.learners import execute_a_series_of_random_forest_learnings"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f507e6bd",
      "metadata": {
        "id": "f507e6bd"
      },
      "source": [
        "# Random Forest Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e14a7e5",
      "metadata": {
        "id": "1e14a7e5"
      },
      "source": [
        "## File: 'headlines_001.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94187795",
      "metadata": {
        "id": "94187795"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "2393095f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2393095f",
        "outputId": "86647aa5-4233-49f5-e6ff-8ef7dfa684b7"
      },
      "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": "90a50f3e",
      "metadata": {
        "id": "90a50f3e"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "ac1ad93b",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ac1ad93b",
        "outputId": "671f56ef-bc92-4247-8c81-e9272bbe22b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 100\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 66.0 %\n",
            "\n",
            "Average precision score: 60.04415683288923 %\n",
            "\n",
            "Average recall score: 63.51351351351351 %\n",
            "\n",
            "Average F1-score: 64.82758620689654 %\n",
            "\n",
            "Matthews correlation coefficient: 0.31977238516555756\n",
            "\n",
            "Mean absolute error: 0.34\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       0.66      0.68      0.67       152\n",
            "           1       0.66      0.64      0.65       148\n",
            "\n",
            "    accuracy                           0.66       300\n",
            "   macro avg       0.66      0.66      0.66       300\n",
            "weighted avg       0.66      0.66      0.66       300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 63.33333333333333 %\n",
            "\n",
            "Average precision score: 57.65553789083201 %\n",
            "\n",
            "Average recall score: 70.27027027027027 %\n",
            "\n",
            "Average F1-score: 65.40880503144655 %\n",
            "\n",
            "Matthews correlation coefficient: 0.2708869916298149\n",
            "\n",
            "Mean absolute error: 0.36666666666666664\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       0.66      0.57      0.61       152\n",
            "           1       0.61      0.70      0.65       148\n",
            "\n",
            "    accuracy                           0.63       300\n",
            "   macro avg       0.64      0.63      0.63       300\n",
            "weighted avg       0.64      0.63      0.63       300\n",
            "\n",
            "\n",
            "[#3]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 65.33333333333333 %\n",
            "\n",
            "Average precision score: 59.269144144144136 %\n",
            "\n",
            "Average recall score: 68.91891891891892 %\n",
            "\n",
            "Average F1-score: 66.23376623376622 %\n",
            "\n",
            "Matthews correlation coefficient: 0.30826870188601263\n",
            "\n",
            "Mean absolute error: 0.3466666666666667\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       0.67      0.62      0.64       152\n",
            "           1       0.64      0.69      0.66       148\n",
            "\n",
            "    accuracy                           0.65       300\n",
            "   macro avg       0.65      0.65      0.65       300\n",
            "weighted avg       0.65      0.65      0.65       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 61.66666666666667 %\n",
            "\n",
            "Average precision score: 56.66463455937141 %\n",
            "\n",
            "Average recall score: 56.08108108108109 %\n",
            "\n",
            "Average F1-score: 59.07473309608542 %\n",
            "\n",
            "Matthews correlation coefficient: 0.23334627636303465\n",
            "\n",
            "Mean absolute error: 0.38333333333333336\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       0.61      0.67      0.64       152\n",
            "           1       0.62      0.56      0.59       148\n",
            "\n",
            "    accuracy                           0.62       300\n",
            "   macro avg       0.62      0.62      0.62       300\n",
            "weighted avg       0.62      0.62      0.62       300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 61.0 %\n",
            "\n",
            "Average precision score: 56.12055220688315 %\n",
            "\n",
            "Average recall score: 57.432432432432435 %\n",
            "\n",
            "Average F1-score: 59.23344947735192 %\n",
            "\n",
            "Matthews correlation coefficient: 0.21963305848019107\n",
            "\n",
            "Mean absolute error: 0.39\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       0.61      0.64      0.63       152\n",
            "           1       0.61      0.57      0.59       148\n",
            "\n",
            "    accuracy                           0.61       300\n",
            "   macro avg       0.61      0.61      0.61       300\n",
            "weighted avg       0.61      0.61      0.61       300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 51.66666666666667 %\n",
            "\n",
            "Average precision score: 49.993069993069994 %\n",
            "\n",
            "Average recall score: 5.405405405405405 %\n",
            "\n",
            "Average F1-score: 9.937888198757763 %\n",
            "\n",
            "Matthews correlation coefficient: 0.05195671402336793\n",
            "\n",
            "Mean absolute error: 0.48333333333333334\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       0.51      0.97      0.67       152\n",
            "           1       0.62      0.05      0.10       148\n",
            "\n",
            "    accuracy                           0.52       300\n",
            "   macro avg       0.56      0.51      0.38       300\n",
            "weighted avg       0.56      0.52      0.39       300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 51.66666666666667 %\n",
            "\n",
            "Average precision score: 50.079794079794084 %\n",
            "\n",
            "Average recall score: 3.3783783783783785 %\n",
            "\n",
            "Average F1-score: 6.451612903225806 %\n",
            "\n",
            "Matthews correlation coefficient: 0.06830971898165714\n",
            "\n",
            "Mean absolute error: 0.48333333333333334\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       0.51      0.99      0.67       152\n",
            "           1       0.71      0.03      0.06       148\n",
            "\n",
            "    accuracy                           0.52       300\n",
            "   macro avg       0.61      0.51      0.37       300\n",
            "weighted avg       0.61      0.52      0.37       300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 72.8 %\n",
            "\n",
            "Average precision score: 67.58857142857143 %\n",
            "\n",
            "Average recall score: 64.8 %\n",
            "\n",
            "Average F1-score: 70.43478260869566 %\n",
            "\n",
            "Matthews correlation coefficient: 0.46195131212967144\n",
            "\n",
            "Mean absolute error: 0.272\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       0.70      0.81      0.75       500\n",
            "           1       0.77      0.65      0.70       500\n",
            "\n",
            "    accuracy                           0.73      1000\n",
            "   macro avg       0.73      0.73      0.73      1000\n",
            "weighted avg       0.73      0.73      0.73      1000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 100\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 72.6 %\n",
            "\n",
            "Average precision score: 67.23906976744188 %\n",
            "\n",
            "Average recall score: 65.60000000000001 %\n",
            "\n",
            "Average F1-score: 70.53763440860216 %\n",
            "\n",
            "Matthews correlation coefficient: 0.4564957972344289\n",
            "\n",
            "Mean absolute error: 0.274\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       0.70      0.80      0.74       500\n",
            "           1       0.76      0.66      0.71       500\n",
            "\n",
            "    accuracy                           0.73      1000\n",
            "   macro avg       0.73      0.73      0.72      1000\n",
            "weighted avg       0.73      0.73      0.72      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 73.1 %\n",
            "\n",
            "Average precision score: 66.8754491017964 %\n",
            "\n",
            "Average recall score: 73.2 %\n",
            "\n",
            "Average F1-score: 73.12687312687312 %\n",
            "\n",
            "Matthews correlation coefficient: 0.46200092400277204\n",
            "\n",
            "Mean absolute error: 0.269\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      0.73      0.73       500\n",
            "           1       0.73      0.73      0.73       500\n",
            "\n",
            "    accuracy                           0.73      1000\n",
            "   macro avg       0.73      0.73      0.73      1000\n",
            "weighted avg       0.73      0.73      0.73      1000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 67.10000000000001 %\n",
            "\n",
            "Average precision score: 61.36705202312138 %\n",
            "\n",
            "Average recall score: 69.0 %\n",
            "\n",
            "Average F1-score: 67.7134445534838 %\n",
            "\n",
            "Matthews correlation coefficient: 0.3422471917408929\n",
            "\n",
            "Mean absolute error: 0.329\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       0.68      0.65      0.66       500\n",
            "           1       0.66      0.69      0.68       500\n",
            "\n",
            "    accuracy                           0.67      1000\n",
            "   macro avg       0.67      0.67      0.67      1000\n",
            "weighted avg       0.67      0.67      0.67      1000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_random_forest_learnings(X=X, Y=Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ffe4c406",
      "metadata": {
        "id": "ffe4c406"
      },
      "source": [
        "## File: 'headlines_004.json'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44d5d5c4",
      "metadata": {
        "id": "44d5d5c4"
      },
      "source": [
        "Reads the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "143bdcbf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "143bdcbf",
        "outputId": "7cac7800-f81b-42e8-d832-25e964e01e6f"
      },
      "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": "22491c5e",
      "metadata": {
        "id": "22491c5e"
      },
      "source": [
        "Executes a series of learnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "5f2f5362",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5f2f5362",
        "outputId": "5e444cff-1014-4a08-95d0-6b4b8cd7cb8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t--------------- TRAIN-TEST SPLIT ---------------\n",
            "\n",
            "- test_size: 0.3\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 100\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.3939393939394 %\n",
            "\n",
            "Average precision score: 95.70705825206443 %\n",
            "\n",
            "Average recall score: 98.0 %\n",
            "\n",
            "Average F1-score: 97.3308504034761 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9479363231151119\n",
            "\n",
            "Mean absolute error: 0.026060606060606062\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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jrZrff7dxLIBDSlwW9thpG/4y/cpSX9bKqM/B3650ESyH5X+bX5Lr1PQ7v4g4uCsLYmbdg4DGGgh+XrTczHIr1cQGks6UNEvSTEk3Slpf0mBJ0yXNkXSzpJ4pb6+0PycdH9SpZ+jMyWZWn0oR/CT1B84AhkXELkAjcAxwCXBpRGwPLAbGplPGAotT+qUpX8efoTMnm1n9yaaoL9l8fj2ADST1AHoDr5L1J9yajk8Cjkrbo9M+6fih6sTLx2JmcpakL0r6dtrfRtI+Hb2hmXV/OWp+fSXNKPiNa7lGRMwDfgS8TBb0lpJ9S7wkIppTtrlk6weR/nwlnduc8m/R0Wco5iPnnwKryKLxBcBbwG3A3h29qZl1bznqW00RMWzd11AfstrcYGAJ2SzxI0tRvmIUE/z2jYg9JT0JEBGLW15Amln9EdCjNL29nwRejIiFAJJuBw4ANpPUI9XuBgDzUv55ZENr56Zm8qbAGx29eTHv/FZIaiT7tg9JW5LVBM2sTpVo6cqXgeGSeqd3d4cCs4F7ySZUARgD3Jm2J6d90vE/pYXMO6SYmt8VwG+BrSRdlG56fkdvaGbdm1Sa4W0RMV3SrcATQDPwJDAB+D1wk6QLU9p16ZTrgF9KmgMsIusZ7rBixvb+WtLjZFFZwFER8Uxnbmpm3VupvnGOiPHA+DWSXwDW6lSNiPeAz5fmzsVNZroN8C7wu8K0iHi5VIUws+6lyqfqK0oxzd7f88FCRuuT9cw8B+xcxnKZWZUSVP1EpcUoptm7a+F+mu3lq61kN7Na1w3W5C1GUQsYFYqIJyTtW47CmFn3oBpYxaOYd37fKNhtAPYESjMvjpl1O/W0dOXGBdvNZO8AbytPccysO6j54Jc+bt44Is7qovKYWTdQ05OZtgwvkXRAVxbIzKpbtnRlpUvReW3V/B4le7/3lKTJZIOO32k5GBG3l7lsZlalanoBowLrkw0ePoQPvvcLwMHPrA7VQ4fHVqmndyYfBL0WHR5MbGbdXw1U/NoMfo3ARrDOD3oc/Mzqlmio8e/8Xo2IC7qsJGbWLYjar/nVwOOZWckJetTAS7+2gt+hXVYKM+s2ar7mFxGLurIgZtZ91MunLmZmH1IDsc/Bz8zyEbWx4HctPIOZdSVlzd5ifu1eStpM0q2SnpX0jKT9JG0uaaqk59OffVJeSbpC0hxJT6e5RTvMwc/McslGeJQm+AGXA3dHxMeA3YBngHOBaRExBJiW9gFGAUPSbxxwdWeew8HPzHJTkb82ryFtChxIWp0tIt6PiCVkC5lPStkmAUel7dHADZF5hGx93607+gwOfmaWW451e/tKmlHwG1dwmcHAQuAXkp6UdK2kDYF+EfFqyvMa0C9t9wdeKTh/bkrrEHd4mFlOyjOfX1NEDGvlWA+ymaNOT2v4Xs4HTVwAIiIklWU4rWt+ZpZLS29vMb92zAXmRsT0tH8rWTB8vaU5m/5ckI7PAwYWnD8gpXWIg5+Z5VaKDo+IeA14RdKOKelQYDYwGRiT0sYAd6btycAJqdd3OLC0oHmcm5u9ZpaPSjqN/enAryX1BF4ATiKrlN0iaSzwEvCFlHcKcAQwB3g35e0wBz8zy6WUHzlHxFPAut4JrjW3QEQEcFqJbu3gZ2b51fQCRmZmren+oc/Bz8xyEtDomp+Z1aMaiH0OfmaWl1ANNHwd/MwsN9f8zKzuZJ+6dP/o5+BnZvnINT8zq1New8PM6k42mWmlS9F5Dn5mlpt7e82sLtVAq9fBr5TeW76CI8ddxvIVzaxsXsmnD92D8049klPOv56nnnmZHj0a2Wvnbbn0W8eyXo/GShe3rvzk7KM4fPgONC15h/1PvgqAc8YczAlH7sUbS98B4HvX/pGp058HYOeP9uPH3/g0G2/Yi1gVHPLln7N8RTOfO2RXvnH8gUQEr77xFqdedBuL3ny3Ys9VKa75tUHSROBfgAURsUu57lNNevXswZ1Xn8FGvXuxonklo770Yz65/1A+P2pvJnwvm57sS+dfzw13PMTYo0dUuLT15ca7n+Sa307nZ+d99kPpV9/6MFfe8pcPpTU2NPDzb32OL3//Nmb+/XX6bLIBK1aupLGhge9/bRTDT7ySRW++y3dPPYxTPrMvl0y6tysfpeJq5Z1fOSczvR4YWcbrVx1JbNS7FwArmleyonklkjjsgJ2Rsqm/99p5W+YvWFzhktafh55+icVvLisq7yF7b8esF15n5t9fB2Dxm8tYtSrSuhRiww3WA2Dj3r147Y03y1bmqlXkRKbV3iNctppfRNwvaVC5rl+tVq5cxUH/fgkvzl3I2M8fyLBdBq0+tqJ5JTdPeZTvf/PoyhXQPuSUz+zDMYftxpN/m8/5P72bpW+/x3YD+hIR3PqDE+i7aW9uv3cmV9z0IM0rV/HNS3/Hg9edxrvvreCFuW9w1uV3VfoRKqK6w1pxKj6NvaRxLSs7LWxaWOnidFpjYwMP/M95zPr9hTwx6yVmz5m/+thZF9/M/ntsz/57bF/BElqLiZMfZY/jL2PEKVfz+htvceFXs4ZKj8YGhu+6LeMuvJVRZ1zHkR/fiQP3/Cg9Ghs4efQ+fGLc1ex09A+Z9cLrnHncgRV+iq5X4nV7K6biwS8iJkTEsIgYtmXfLStdnJLZdOPejNhrB6Y9PBuAS66ZQtOSt7nozM+2c6Z1lYWL32HVqiAimHTX4+z1sWwVxPkLl/LQ0/9g0Zvvsmz5CqZO/xu7DdmaXbf/CAD/mJ+9trjjvpnsu/PAVq9fy0qxbm+lVTz41ZKmxW+x9K2s52/Ze+9z76PPMmRQP2644yGmPfwM1154Ig0N/iuvFv0232j19r+M2IlnXswWCZv22ByGDu7HBr3Wo7GhgQN2G8RzLy3k1aa32HHbLdli094AHLTXdjz3cvdvrXRICaOfpMa0bu9daX+wpOmS5ki6Oa3vgaReaX9OOj6oM4/gT11K6LWmN/nqd37JylWrWLUq+Mwn92TkiF3pO/wMBn5kcw47+b8B+NeDd+fsU0ZVuLT15drzj+aA3Qezxaa9mXnLN7n4+nv5+G6D2HX7rYkIXn5tCWf+eDIAS99+j5/+5iGm/exUiGDq9Of5wyN/A+AHk+7l95ePpbl5Ja+8vpSvXnJ7JR+rYkrcpP068AywSdq/BLg0Im6S9DNgLHB1+nNxRGwv6ZiU7986elNla4KUnqQbgYOAvsDrwPiIuK6tc/baa1j8ZfqMspTHyqPPwd+udBEsh+VPXcuqt+Z3KnLttOseccOd9xWVd5/tNnu8jUXLkTQAmARcBHwD+FdgIfCRiGiWtB/wnYg4XNI9afthST2A14Ato4NBrJy9vceW69pmVmHFh8++kgprNBMiYkLB/mXA2cDGaX8LYElENKf9uUD/tN0feAUgBcalKX9T7vLjZq+Z5ZS9zis6+jW1VvOT1DII4nFJB5WoeEVz8DOzfEo3n98BwKclHQGsT/bO73JgM0k9Uu1vADAv5Z8HDATmpmbvpsAbHb25ux7NLLdSdPZGxHkRMSAiBgHHAH+KiOOBe4GWkQBjgDvT9uS0Tzr+p46+7wMHPzPLTauHa7b366BzgG9ImkP2Tq+lo/Q6YIuU/g3g3M48hZu9ZpZbqQdvRMR9wH1p+wVgn3XkeQ/4fKnu6eBnZrl0h9EbxXDwM7P8aiD6OfiZWW6ezNTM6lKVT9hSFAc/M8vH6/aaWb1ys9fM6o5wzc/M6lQNxD4HPzPrgBqIfg5+ZpZbta/PUQwHPzPLrfuHPgc/M+uIGoh+Dn5mlkvOyUyrloOfmeXjj5zNrF7VQOxz8DOzvDo1UWnVcPAzs9xqIPY5+JlZPrUymanX8DCz/EqwgpGkgZLulTRb0ixJX0/pm0uaKun59GeflC5JV0iaI+lpSXt25hEc/MwsNxX5XzuagW9GxFBgOHCapKFkCxNNi4ghwDQ+WKhoFDAk/cYBV3fmGRz8zCw3qbhfWyLi1Yh4Im2/BTwD9AdGA5NStknAUWl7NHBDZB4hW993644+g9/5mVk+gobiX/r1lTSjYH9CRExY65LSIGAPYDrQLyJeTYdeA/ql7f7AKwWnzU1pr9IBDn5m1gFFR7+miBjW5pWkjYDbgP+IiDcLP6OJiJDU4YXJ2+Jmr5nl0jKZaWebvQCS1iMLfL+OiNtT8ustzdn054KUPg8YWHD6gJTWIQ5+ZpZbCTp7UVbFuw54JiJ+XHBoMjAmbY8B7ixIPyH1+g4HlhY0j3Nzs9fMcivRR84HAP8O/K+kp1Lat4CLgVskjQVeAr6Qjk0BjgDmAO8CJ3Xm5g5+ZpZbKYa3RcSDtF5BPHQd+QM4rdM3Thz8zCy3Whjh4eBnZrkU25lR7Rz8zCw3T2ZqZvWp+8c+Bz8zy68GYp+Dn5nlJS9daWb1p2WER3fnER5mVpdc8zOz3Gqh5ufgZ2a5+VMXM6s//sjZzOpRrXR4OPiZWW5u9ppZXXLNz8zqUg3EPgc/M+uAGoh+Dn5mlougJoa3KZsctTpIWkg2bXWt6Qs0VboQlkut/pttGxFbduYCku4m+/spRlNEjOzM/cqlqoJfrZI0o73l+6y6+N+s9nlsr5nVJQc/M6tLDn5dY0KlC2C5+d+sxvmdn5nVJdf8zKwuOfiZWV1y8CsjSSMlPSdpjqRzK10ea5+kiZIWSJpZ6bJYeTn4lYmkRuAqYBQwFDhW0tDKlsqKcD1QlR/lWmk5+JXPPsCciHghIt4HbgJGV7hM1o6IuB9YVOlyWPk5+JVPf+CVgv25Kc3MqoCDn5nVJQe/8pkHDCzYH5DSzKwKOPiVz2PAEEmDJfUEjgEmV7hMZpY4+JVJRDQDXwPuAZ4BbomIWZUtlbVH0o3Aw8COkuZKGlvpMll5eHibmdUl1/zMrC45+JlZXXLwM7O65OBnZnXJwc/M6pKDXzciaaWkpyTNlPQbSb07ca3rJR2dtq9ta9IFSQdJ2r8D9/iHpLVW+WotfY08b+e813cknZW3jFa/HPy6l2URsXtE7AK8D3y58KCkDq3DHBFfiojZbWQ5CMgd/MyqmYNf9/UAsH2qlT0gaTIwW1KjpB9KekzS05JOBVDmyjS/4B+BrVouJOk+ScPS9khJT0j6q6RpkgaRBdkzU61zhKQtJd2W7vGYpAPSuVtI+oOkWZKuJVvfuk2S7pD0eDpn3BrHLk3p0yRtmdK2k3R3OucBSR8rxV+m1Z8O1RSsslINbxRwd0raE9glIl5MAWRpROwtqRfwF0l/APYAdiSbW7AfMBuYuMZ1twSuAQ5M19o8IhZJ+hnwdkT8KOX7H+DSiHhQ0jZko1h2AsYDD0bEBZKOBIoZHXFyuscGwGOSbouIN4ANgRkRcaakb6drf41sYaEvR8TzkvYFfgoc0oG/RqtzDn7dywaSnkrbDwDXkTVHH42IF1P6YcA/t7zPAzYFhgAHAjdGxEpgvqQ/reP6w4H7W64VEa3Na/dJYKi0umK3iaSN0j0+m879vaTFRTzTGZI+k7YHprK+AawCbk7pvwJuT/fYH/hNwb17FXEPs7U4+HUvyyJi98KEFATeKUwCTo+Ie9bId0QJy9EADI+I99ZRlqJJOogskO4XEe9Kug9Yv5Xske67ZM2/A7OO8Du/2nMP8BVJ6wFI2kHShsD9wL+ld4JbAwev49xHgAMlDU7nbp7S3wI2Lsj3B+D0lh1JLcHofuC4lDYK6NNOWTcFFqfA9zGymmeLBqCl9nocWXP6TeBFSZ9P95Ck3dq5h9k6OfjVnmvJ3uc9kRbh+TlZDf+3wPPp2A1kM5d8SEQsBMaRNTH/ygfNzt8Bn2np8ADOAIalDpXZfNDr/F2y4DmLrPn7cjtlvRvoIekZ4GKy4NviHWCf9AyHABek9OOBsal8s/DSANZBntXFzOqSa35mVpcc/MysLjn4mVldcvAzs7rk4GdmdcnBz8zqkoOfmdWl/w+OzJO72gr7iAAAAABJRU5ErkJggg==\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      0.97      0.97      1700\n",
            "           1       0.97      0.98      0.97      1600\n",
            "\n",
            "    accuracy                           0.97      3300\n",
            "   macro avg       0.97      0.97      0.97      3300\n",
            "weighted avg       0.97      0.97      0.97      3300\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.3939393939394 %\n",
            "\n",
            "Average precision score: 95.6024725785462 %\n",
            "\n",
            "Average recall score: 98.25 %\n",
            "\n",
            "Average F1-score: 97.33746130030958 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9480164613131116\n",
            "\n",
            "Mean absolute error: 0.026060606060606062\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      0.97      0.97      1700\n",
            "           1       0.96      0.98      0.97      1600\n",
            "\n",
            "    accuracy                           0.97      3300\n",
            "   macro avg       0.97      0.97      0.97      3300\n",
            "weighted avg       0.97      0.97      0.97      3300\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.24242424242424 %\n",
            "\n",
            "Average precision score: 94.78759080067219 %\n",
            "\n",
            "Average recall score: 99.5625 %\n",
            "\n",
            "Average F1-score: 97.22306988098872 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9459053445271602\n",
            "\n",
            "Mean absolute error: 0.027575757575757576\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      0.95      0.97      1700\n",
            "           1       0.95      1.00      0.97      1600\n",
            "\n",
            "    accuracy                           0.97      3300\n",
            "   macro avg       0.97      0.97      0.97      3300\n",
            "weighted avg       0.97      0.97      0.97      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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 51.54545454545455 %\n",
            "\n",
            "Average precision score: 48.50757575757576 %\n",
            "\n",
            "Average recall score: 0.125 %\n",
            "\n",
            "Average F1-score: 0.2495321272613849 %\n",
            "\n",
            "Matthews correlation coefficient: 0.010974075712340955\n",
            "\n",
            "Mean absolute error: 0.48454545454545456\n",
            "\n",
            "Confusion matrix:\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"
          ]
        },
        {
          "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       0.52      1.00      0.68      1700\n",
            "           1       0.67      0.00      0.00      1600\n",
            "\n",
            "    accuracy                           0.52      3300\n",
            "   macro avg       0.59      0.50      0.34      3300\n",
            "weighted avg       0.59      0.52      0.35      3300\n",
            "\n",
            "\n",
            "\t--------------- K-FOLD ---------------\n",
            "\n",
            "- n_folds: 10\n",
            "\n",
            "\t\t=============== BoW ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.60909090909091 %\n",
            "\n",
            "Average precision score: 95.93581158906832 %\n",
            "\n",
            "Average recall score: 98.48654708520179 %\n",
            "\n",
            "Average F1-score: 97.56594169366033 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9523314813742632\n",
            "\n",
            "Mean absolute error: 0.023909090909090908\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      0.97      0.98      5648\n",
            "           1       0.97      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",
            "\n",
            "\t\t=============== Tf-idf ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 75\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.23636363636363 %\n",
            "\n",
            "Average precision score: 94.8948694089757 %\n",
            "\n",
            "Average recall score: 99.30866965620328 %\n",
            "\n",
            "Average F1-score: 97.21968172672398 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9455759297408918\n",
            "\n",
            "Mean absolute error: 0.027636363636363636\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      0.95      0.97      5648\n",
            "           1       0.95      0.99      0.97      5352\n",
            "\n",
            "    accuracy                           0.97     11000\n",
            "   macro avg       0.97      0.97      0.97     11000\n",
            "weighted avg       0.97      0.97      0.97     11000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 100\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 97.20909090909092 %\n",
            "\n",
            "Average precision score: 94.82198652742264 %\n",
            "\n",
            "Average recall score: 99.36472346786248 %\n",
            "\n",
            "Average F1-score: 97.19455359590606 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9450985991984127\n",
            "\n",
            "Mean absolute error: 0.027909090909090908\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      0.95      0.97      5648\n",
            "           1       0.95      0.99      0.97      5352\n",
            "\n",
            "    accuracy                           0.97     11000\n",
            "   macro avg       0.97      0.97      0.97     11000\n",
            "weighted avg       0.97      0.97      0.97     11000\n",
            "\n",
            "\n",
            "\n",
            "\t\t=============== Word2Vec ===============\n",
            "\n",
            "[#1]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 100\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 96.45454545454545 %\n",
            "\n",
            "Average precision score: 93.74058357488234 %\n",
            "\n",
            "Average recall score: 98.56128550074739 %\n",
            "\n",
            "Average F1-score: 96.43510054844607 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9299772149795642\n",
            "\n",
            "Mean absolute error: 0.035454545454545454\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      0.94      0.96      5648\n",
            "           1       0.94      0.99      0.96      5352\n",
            "\n",
            "    accuracy                           0.96     11000\n",
            "   macro avg       0.96      0.97      0.96     11000\n",
            "weighted avg       0.97      0.96      0.96     11000\n",
            "\n",
            "\n",
            "[#2]\n",
            "\n",
            "* Parameters:\n",
            "\n",
            "- Model: Random Forest Classifier\n",
            "- bootstrap: True\n",
            "- ccp_alpha: 0.0\n",
            "- class_weight: None\n",
            "- criterion: gini\n",
            "- max_depth: None\n",
            "- max_features: auto\n",
            "- max_leaf_nodes: None\n",
            "- max_samples: None\n",
            "- min_impurity_decrease: 0.0\n",
            "- min_samples_leaf: 1\n",
            "- min_samples_split: 2\n",
            "- min_weight_fraction_leaf: 0.0\n",
            "- n_estimators: 125\n",
            "- n_jobs: None\n",
            "- oob_score: False\n",
            "- random_state: None\n",
            "- verbose: 0\n",
            "- warm_start: False\n",
            "\n",
            "Accuracy: 96.44545454545454 %\n",
            "\n",
            "Average precision score: 93.6813747540229 %\n",
            "\n",
            "Average recall score: 98.67339312406577 %\n",
            "\n",
            "Average F1-score: 96.43020177120422 %\n",
            "\n",
            "Matthews correlation coefficient: 0.9298967171440534\n",
            "\n",
            "Mean absolute error: 0.03554545454545455\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      0.94      0.96      5648\n",
            "           1       0.94      0.99      0.96      5352\n",
            "\n",
            "    accuracy                           0.96     11000\n",
            "   macro avg       0.96      0.97      0.96     11000\n",
            "weighted avg       0.97      0.96      0.96     11000\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Executes a series of learnings\n",
        "execute_a_series_of_random_forest_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": "Random_Forest.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}