{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3c5cc1f",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/bmalcover/AppOC/blob/9b2180b359088c6a3cace2ad7d8f05bb545d2c24/docs/notebooks/01_ML/03_Trees.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5022c3d9",
   "metadata": {},
   "source": [
    "# Decision Trees, Random Forest and XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41682e13",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">     \n",
    "<b> Table of Contents </b>\n",
    "\n",
    "1. [Introduction](#introduction)\n",
    "2. [Decision Tree](#dt)\n",
    "3. [Random Forest](#rf)\n",
    "4. [XGBoost](#XGBoost)\n",
    "5. [Exercise](#exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb9fd62",
   "metadata": {},
   "source": [
    "In this notebook we will use XGBoost. We need another library: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "953b1d65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\"uv\" no se reconoce como un comando interno o externo,\n",
      "programa o archivo por lotes ejecutable.\n"
     ]
    }
   ],
   "source": [
    "!uv pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "29de8ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffcc238",
   "metadata": {},
   "source": [
    "The dataset used is the Wine dataset, a classic dataset in machine learning containing chemical properties of wines and their corresponding classes.\n",
    "\n",
    "Let's load and prepare the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "0e36d22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine()\n",
    "X = wine.data\n",
    "y = wine.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ab5555",
   "metadata": {},
   "source": [
    "We divide the data into train and test: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "db84ecca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4dcdef",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "    <b> IV. <i>Decision Tree </i></b>\n",
    "    <a id=\"dt\"></a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab6bab0",
   "metadata": {},
   "source": [
    "A Decision Tree is a supervised machine learning algorithm. It works by splitting the data into smaller subsets based on feature values, forming a tree-like structure of decisions. Each node represents a decision based on a feature, each branch represents the outcome of that decision and each leaf represents a final prediction.\n",
    "\n",
    "\n",
    "We can put the following parameters: \n",
    "- ``random_state``: ensures same tree each run.\n",
    "\n",
    "- ``max_depth``: limits how deep the tree can grow preventing the model from becoming too complex. If we use a small depth, the model is simpler and may underfit. If we use a larger depth, the model can become too complex and overfit. \n",
    "\n",
    "- ``min_samples_split``: minimum samples to split a node. If we use a higher value, there will be fewer splits and the tree is simpler. Otherwise, the tree becomes more complex. This value helps to control overfitting. \n",
    "\n",
    "- ``criterion``: It is a function used to measure the quality of a split. It usually takes \"gini\" or \"entropy\". Gini is more commonly used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "9cc17fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950d7939",
   "metadata": {},
   "source": [
    "Let's train with the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f5222f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-24 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-24 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-24 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-24 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-24 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-24 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-24 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-24 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-24 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-24 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-24 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-24 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-24 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-24 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-24 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d375ffec",
   "metadata": {},
   "source": [
    "Perform predictions on the evaluation set: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "81fbccf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8a19ad",
   "metadata": {},
   "source": [
    "Then, we evaluate the performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c2cc4424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 0.9629629629629629\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95        19\n",
      "           1       0.95      1.00      0.98        21\n",
      "           2       1.00      0.93      0.96        14\n",
      "\n",
      "    accuracy                           0.96        54\n",
      "   macro avg       0.97      0.96      0.96        54\n",
      "weighted avg       0.96      0.96      0.96        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree Accuracy:\", accuracy_score(y_test, y_pred_dt))\n",
    "print(classification_report(y_test, y_pred_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac90a91",
   "metadata": {},
   "source": [
    "We can visualize the decision tree: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "73507d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plot_tree(\n",
    "    dt,\n",
    "    feature_names=wine.feature_names,\n",
    "    class_names=wine.target_names,\n",
    "    filled=True\n",
    ")\n",
    "plt.title(\"Decision Tree Visualization\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e28e38",
   "metadata": {},
   "source": [
    "The model is making decisions by asking questions about the features. Each box shows the condition (for example, ``color intensity``), the number of samples ( ``samples``), number of samples per class (``value``) and the predicted class by majority (``class``). The ``gini`` value measures how mixed the classes are in a node, if it is closer to 0 the data is mostly one class. A high gini the data is more mixed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee673e4c",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "_Try different values of max_depth, min_samples_split. You can use GridSearchCV (remember to import the library)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e16736cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "857a39a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the tree "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77cd7046",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "    <b> IV. <i>Random Forest </i></b>\n",
    "    <a id=\"Exercise\"></a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8edac1e4",
   "metadata": {},
   "source": [
    "A Random Forest is an ensemble learning method that builds many decision trees and combines their predictions. Instead of relying on a single tree, trains multiple trees on different subsets of the data. \n",
    "\n",
    "- ``n_estimators``: Number of trees in the forest. More trees usually brings better performance. \n",
    "- ``random_state``: Controls randomness.\n",
    "- ``max_depth``: Maximum depth of each tree.\n",
    "- ``min_samples_split``: Minimum number of samples required to split a node.\n",
    "- ``max_features``: Number of features considered at each split. There are different options: ``sqrt``(compute the square root), ``log2``(compute the logarithm of the features $log_2$(features)), fixed number or a percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8b3e951e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d29b6f",
   "metadata": {},
   "source": [
    "Let's train the random forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "eb9ebd27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-25 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-25 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-25 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-25 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-25 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-25 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-25 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-25 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-25 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-25 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-25 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-25 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-25 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8df2229",
   "metadata": {},
   "source": [
    "Perform predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "d699cfdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb2dce6",
   "metadata": {},
   "source": [
    "Evaluate the model: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "42bc98fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        21\n",
      "           2       1.00      1.00      1.00        14\n",
      "\n",
      "    accuracy                           1.00        54\n",
      "   macro avg       1.00      1.00      1.00        54\n",
      "weighted avg       1.00      1.00      1.00        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest Accuracy:\", accuracy_score(y_test, y_pred_rf))\n",
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c283a2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "_Try different values of n_estimators,max_features, max_depth, min_samples_split. You can use GridSearchCV_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514e3d5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b87fad37",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "    <b> IV. <i>XGBoost </i></b>\n",
    "    <a id=\"XGBoost\"></a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c814ea99",
   "metadata": {},
   "source": [
    "XGBoost (Extreme Gradient Boosting) is an advanced ensemble learning algorithm based on boosting.\n",
    "\n",
    "__What is boosting?__\n",
    "\n",
    "Boosting is a machine learning technique that combines multiple weak models (usually small decision trees) to create a strong, accurate model. It trains a simple model, checks where it makes mistakes, gives more importance to those mistakes and trains a new model focusing on those errors. It repeats this process multiple times. Then, combines all the models into one final prediction. \n",
    "\n",
    "The parameters used are: \n",
    "- ``n_estimators``: Number of trees or boosting rounds. The more trees, better learning but we risk to overfitting. \n",
    "- ``max_depth``: Maximum depth of each tree.\n",
    "- ``learning_rate``:  Step size for updating predictions. Giving a small value (0.01), learns slowly but can obtain better accuracy. Using a large value, learns faster but can miss an optimal solution. \n",
    "- ``eval_metric``: Evaluation metric used during training. We have ``auc`` for binary classification, ``mlogloss`` is multiclass log loss and measures how well predicted the probabilities match the ture labels. \n",
    "- ``random_state``: Controls randomness.\n",
    "- ``subsample``: fraction of samples used per tree.\n",
    "- ``colsample_bytree``: Fraction of features (columns) used for each tree\n",
    "- ``gamma``: Minimum loss reduction required to make a split. Controls how strict the model is when creating splits. If we put a 0, the model splits more easily.  \n",
    "\n",
    "There are much more parameters available: [Link to documentation](https://xgboost.readthedocs.io/en/stable/parameter.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "104921ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(\n",
    "    n_estimators=100,\n",
    "    max_depth=3,\n",
    "    learning_rate=0.1,\n",
    "    eval_metric='auc',\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f3c172e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-26 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-26 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-26 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-26 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-26 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-26 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-26 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-26 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-26 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='auc', feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='multi:softprob', ...)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ea78852d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb = xgb.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "be51448e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy: 0.9814814814814815\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.95      1.00      0.98        21\n",
      "           2       1.00      0.93      0.96        14\n",
      "\n",
      "    accuracy                           0.98        54\n",
      "   macro avg       0.98      0.98      0.98        54\n",
      "weighted avg       0.98      0.98      0.98        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XGBoost Accuracy:\", accuracy_score(y_test, y_pred_xgb))\n",
    "print(classification_report(y_test, y_pred_xgb))"
   ]
  }
 ],
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