15_PCA - Extracción de características.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/1ahHDNPMOHIMI1K1NdSqSXNal13XXSrHi\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso práctico: _Principal Component Analysis_ (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso de uso práctico se presenta un mecanismo de extracción de características (reducción de dimensionalidad) mediante el uso del algoritmo PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos: Detección de malware en Android"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descripción\n",
    "The sophisticated and advanced Android malware is able to identify the presence of the emulator used by the malware analyst and in response, alter its behavior to evade detection. To overcome this issue, we installed the Android applications on the real device and captured its network traffic. See our publicly available Android Sandbox.\n",
    "\n",
    "CICAAGM dataset is captured by installing the Android apps on the real smartphones semi-automated. The dataset is generated from 1900 applications with the following three categories:\n",
    "\n",
    "**1. Adware (250 apps)**\n",
    "* Airpush: Designed to deliver unsolicited advertisements to the user’s systems for information stealing.\n",
    "* Dowgin: Designed as an advertisement library that can also steal the user’s information.\n",
    "* Kemoge: Designed to take over a user’s Android device. This adware is a hybrid of botnet and disguises itself as popular apps via repackaging.\n",
    "* Mobidash: Designed to display ads and to compromise user’s personal information.\n",
    "* Shuanet: Similar to Kemoge, Shuanet also is designed to take over a user’s device.\n",
    "\n",
    "**2. General Malware (150 apps)**\n",
    "* AVpass: Designed to be distributed in the guise of a Clock app.\n",
    "* FakeAV: Designed as a scam that tricks user to purchase a full version of the software in order to re-mediate non-existing infections.\n",
    "* FakeFlash/FakePlayer: Designed as a fake Flash app in order to direct users to a website (after successfully installed).\n",
    "* GGtracker: Designed for SMS fraud (sends SMS messages to a premium-rate number) and information stealing.\n",
    "* Penetho: Designed as a fake service (hacktool for Android devices that can be used to crack the WiFi password). The malware is also able to infect the user’s computer via infected email attachment, fake updates, external media and infected documents.\n",
    "\n",
    "**3. Benign (1500 apps)**\n",
    "* 2015 GooglePlay market (top free popular and top free new)\n",
    "* 2016 GooglePlay market (top free popular and top free new)\n",
    "\n",
    "### Ficheros de datos\n",
    "* pcap files – the network traffic of both the malware and benign (20% malware and 80% benign)\n",
    "* <span style=\"color:green\">.csv files - the list of extracted network traffic features generated by the CIC-flowmeter</span>\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://www.unb.ca/cic/datasets/android-adware.html\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_Arash Habibi Lashkari, Andi Fitriah A. Kadir, Hugo Gonzalez, Kenneth Fon Mbah and Ali A. Ghorbani, “Towards a Network-Based Framework for Android Malware Detection and Characterization”, In the proceeding of the 15th International Conference on Privacy, Security and Trust, PST, Calgary, Canada, 2017._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignoramos algunos warnings que se producen por invocar el modelo sin el nombre de las características\n",
    "warnings.filterwarnings('ignore', category=UserWarning, message='.*X does not have valid feature names.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construcción de una función que realice el particionado completo\n",
    "def train_val_test_split(df, rstate=42, shuffle=True, stratify=None):\n",
    "    strat = df[stratify] if stratify else None\n",
    "    train_set, test_set = train_test_split(\n",
    "        df, test_size=0.4, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    strat = test_set[stratify] if stratify else None\n",
    "    val_set, test_set = train_test_split(\n",
    "        test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    return (train_set, val_set, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_labels(df, label_name):\n",
    "    X = df.drop(label_name, axis=1)\n",
    "    y = df[label_name].copy()\n",
    "    return (X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/TotalFeatures-ISCXFlowMeter.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualización del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>total_fpackets</th>\n",
       "      <th>total_bpackets</th>\n",
       "      <th>total_fpktl</th>\n",
       "      <th>total_bpktl</th>\n",
       "      <th>min_fpktl</th>\n",
       "      <th>min_bpktl</th>\n",
       "      <th>max_fpktl</th>\n",
       "      <th>max_bpktl</th>\n",
       "      <th>mean_fpktl</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_idle</th>\n",
       "      <th>max_idle</th>\n",
       "      <th>std_idle</th>\n",
       "      <th>FFNEPD</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>calss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1020586</td>\n",
       "      <td>668</td>\n",
       "      <td>1641</td>\n",
       "      <td>35692</td>\n",
       "      <td>2276876</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>679</td>\n",
       "      <td>1390</td>\n",
       "      <td>53.431138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4194240</td>\n",
       "      <td>1853440</td>\n",
       "      <td>1640</td>\n",
       "      <td>668</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80794</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>124</td>\n",
       "      <td>75</td>\n",
       "      <td>124</td>\n",
       "      <td>75</td>\n",
       "      <td>124</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>998</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1</td>\n",
       "      <td>83</td>\n",
       "      <td>-1</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>101888</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189868</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1448</td>\n",
       "      <td>6200</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>706</td>\n",
       "      <td>1390</td>\n",
       "      <td>160.888889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4194240</td>\n",
       "      <td>2722560</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110577</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>528</td>\n",
       "      <td>1422</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>331</td>\n",
       "      <td>1005</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>155136</td>\n",
       "      <td>31232</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>261876</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1618</td>\n",
       "      <td>882</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>730</td>\n",
       "      <td>477</td>\n",
       "      <td>231.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4194240</td>\n",
       "      <td>926720</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1</td>\n",
       "      <td>52</td>\n",
       "      <td>-1</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5824</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29675</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>213</td>\n",
       "      <td>71</td>\n",
       "      <td>213</td>\n",
       "      <td>71</td>\n",
       "      <td>213</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>806635</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1</td>\n",
       "      <td>83</td>\n",
       "      <td>-1</td>\n",
       "      <td>59.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>107008</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>56620</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1074</td>\n",
       "      <td>719</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>592</td>\n",
       "      <td>667</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>128512</td>\n",
       "      <td>10816</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  total_fpackets  total_bpackets  total_fpktl  total_bpktl  \\\n",
       "0   1020586             668            1641        35692      2276876   \n",
       "1     80794               1               1           75          124   \n",
       "2       998               3               0          187            0   \n",
       "3    189868               9               9         1448         6200   \n",
       "4    110577               4               6          528         1422   \n",
       "5    261876               7               6         1618          882   \n",
       "6        14               2               0          104            0   \n",
       "7     29675               1               1           71          213   \n",
       "8    806635               4               0          239            0   \n",
       "9     56620               3               2         1074          719   \n",
       "\n",
       "   min_fpktl  min_bpktl  max_fpktl  max_bpktl  mean_fpktl  ...  mean_idle  \\\n",
       "0         52         52        679       1390   53.431138  ...        0.0   \n",
       "1         75        124         75        124   75.000000  ...        0.0   \n",
       "2         52         -1         83         -1   62.333333  ...        0.0   \n",
       "3         52         52        706       1390  160.888889  ...        0.0   \n",
       "4         52         52        331       1005  132.000000  ...        0.0   \n",
       "5         52         52        730        477  231.142857  ...        0.0   \n",
       "6         52         -1         52         -1   52.000000  ...        0.0   \n",
       "7         71        213         71        213   71.000000  ...        0.0   \n",
       "8         52         -1         83         -1   59.750000  ...        0.0   \n",
       "9         52         52        592        667  358.000000  ...        0.0   \n",
       "\n",
       "   max_idle  std_idle  FFNEPD  Init_Win_bytes_forward  \\\n",
       "0        -1       0.0       2                 4194240   \n",
       "1        -1       0.0       2                       0   \n",
       "2        -1       0.0       4                  101888   \n",
       "3        -1       0.0       2                 4194240   \n",
       "4        -1       0.0       2                  155136   \n",
       "5        -1       0.0       2                 4194240   \n",
       "6        -1       0.0       3                    5824   \n",
       "7        -1       0.0       2                       0   \n",
       "8        -1       0.0       5                  107008   \n",
       "9        -1       0.0       3                  128512   \n",
       "\n",
       "   Init_Win_bytes_backward  RRT_samples_clnt  Act_data_pkt_forward  \\\n",
       "0                  1853440              1640                   668   \n",
       "1                        0                 0                     1   \n",
       "2                       -1                 0                     3   \n",
       "3                  2722560                 8                     9   \n",
       "4                    31232                 5                     4   \n",
       "5                   926720                 3                     7   \n",
       "6                       -1                 0                     2   \n",
       "7                        0                 0                     1   \n",
       "8                       -1                 0                     4   \n",
       "9                    10816                 1                     3   \n",
       "\n",
       "   min_seg_size_forward   calss  \n",
       "0                    32  benign  \n",
       "1                     0  benign  \n",
       "2                    32  benign  \n",
       "3                    32  benign  \n",
       "4                    32  benign  \n",
       "5                    32  benign  \n",
       "6                    32  benign  \n",
       "7                     0  benign  \n",
       "8                    32  benign  \n",
       "9                    32  benign  \n",
       "\n",
       "[10 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>total_fpackets</th>\n",
       "      <th>total_bpackets</th>\n",
       "      <th>total_fpktl</th>\n",
       "      <th>total_bpktl</th>\n",
       "      <th>min_fpktl</th>\n",
       "      <th>min_bpktl</th>\n",
       "      <th>max_fpktl</th>\n",
       "      <th>max_bpktl</th>\n",
       "      <th>mean_fpktl</th>\n",
       "      <th>...</th>\n",
       "      <th>min_idle</th>\n",
       "      <th>mean_idle</th>\n",
       "      <th>max_idle</th>\n",
       "      <th>std_idle</th>\n",
       "      <th>FFNEPD</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>6.319550e+05</td>\n",
       "      <td>631955.000000</td>\n",
       "      <td>631955.00000</td>\n",
       "      <td>631955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.195245e+07</td>\n",
       "      <td>6.728514</td>\n",
       "      <td>10.431934</td>\n",
       "      <td>9.540172e+02</td>\n",
       "      <td>1.206042e+04</td>\n",
       "      <td>141.475727</td>\n",
       "      <td>44.357688</td>\n",
       "      <td>263.675901</td>\n",
       "      <td>183.248084</td>\n",
       "      <td>174.959706</td>\n",
       "      <td>...</td>\n",
       "      <td>1.997327e+07</td>\n",
       "      <td>2.031228e+07</td>\n",
       "      <td>2.075238e+07</td>\n",
       "      <td>4.663875e+05</td>\n",
       "      <td>2.360896</td>\n",
       "      <td>9.620796e+05</td>\n",
       "      <td>3.104519e+05</td>\n",
       "      <td>9.733144</td>\n",
       "      <td>6.72471</td>\n",
       "      <td>19.965713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.900578e+08</td>\n",
       "      <td>174.161354</td>\n",
       "      <td>349.424019</td>\n",
       "      <td>8.235040e+04</td>\n",
       "      <td>4.824716e+05</td>\n",
       "      <td>157.680880</td>\n",
       "      <td>89.099554</td>\n",
       "      <td>289.644383</td>\n",
       "      <td>371.863224</td>\n",
       "      <td>162.024811</td>\n",
       "      <td>...</td>\n",
       "      <td>1.897986e+08</td>\n",
       "      <td>1.897902e+08</td>\n",
       "      <td>1.899721e+08</td>\n",
       "      <td>6.199704e+06</td>\n",
       "      <td>3.041810</td>\n",
       "      <td>1.705655e+06</td>\n",
       "      <td>6.647956e+05</td>\n",
       "      <td>347.877923</td>\n",
       "      <td>174.13813</td>\n",
       "      <td>14.914261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.800000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.900000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.445000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.840000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.761600e+04</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.759751e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.270000e+02</td>\n",
       "      <td>1.670000e+02</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.013498e+06</td>\n",
       "      <td>1.291379e+06</td>\n",
       "      <td>1.306116e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.046400e+05</td>\n",
       "      <td>9.049600e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.431076e+10</td>\n",
       "      <td>48255.000000</td>\n",
       "      <td>74768.000000</td>\n",
       "      <td>4.049644e+07</td>\n",
       "      <td>1.039222e+08</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.431072e+10</td>\n",
       "      <td>4.430000e+10</td>\n",
       "      <td>4.431072e+10</td>\n",
       "      <td>8.470000e+08</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>4.194240e+06</td>\n",
       "      <td>4.194240e+06</td>\n",
       "      <td>74524.000000</td>\n",
       "      <td>48255.00000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration  total_fpackets  total_bpackets   total_fpktl  \\\n",
       "count  6.319550e+05   631955.000000   631955.000000  6.319550e+05   \n",
       "mean   2.195245e+07        6.728514       10.431934  9.540172e+02   \n",
       "std    1.900578e+08      174.161354      349.424019  8.235040e+04   \n",
       "min   -1.800000e+01        0.000000        0.000000  0.000000e+00   \n",
       "25%    0.000000e+00        1.000000        0.000000  6.900000e+01   \n",
       "50%    2.445000e+04        1.000000        0.000000  1.840000e+02   \n",
       "75%    1.759751e+06        3.000000        1.000000  4.270000e+02   \n",
       "max    4.431076e+10    48255.000000    74768.000000  4.049644e+07   \n",
       "\n",
       "        total_bpktl      min_fpktl      min_bpktl      max_fpktl  \\\n",
       "count  6.319550e+05  631955.000000  631955.000000  631955.000000   \n",
       "mean   1.206042e+04     141.475727      44.357688     263.675901   \n",
       "std    4.824716e+05     157.680880      89.099554     289.644383   \n",
       "min    0.000000e+00      -1.000000      -1.000000      -1.000000   \n",
       "25%    0.000000e+00      52.000000      -1.000000      52.000000   \n",
       "50%    0.000000e+00      52.000000      -1.000000      83.000000   \n",
       "75%    1.670000e+02     108.000000      52.000000     421.000000   \n",
       "max    1.039222e+08    1390.000000    1390.000000    1500.000000   \n",
       "\n",
       "           max_bpktl     mean_fpktl  ...      min_idle     mean_idle  \\\n",
       "count  631955.000000  631955.000000  ...  6.319550e+05  6.319550e+05   \n",
       "mean      183.248084     174.959706  ...  1.997327e+07  2.031228e+07   \n",
       "std       371.863224     162.024811  ...  1.897986e+08  1.897902e+08   \n",
       "min        -1.000000       0.000000  ... -1.000000e+00  0.000000e+00   \n",
       "25%        -1.000000      52.000000  ... -1.000000e+00  0.000000e+00   \n",
       "50%        -1.000000      83.000000  ... -1.000000e+00  0.000000e+00   \n",
       "75%       115.000000     356.000000  ...  1.013498e+06  1.291379e+06   \n",
       "max      1390.000000    1390.000000  ...  4.431072e+10  4.430000e+10   \n",
       "\n",
       "           max_idle      std_idle         FFNEPD  Init_Win_bytes_forward  \\\n",
       "count  6.319550e+05  6.319550e+05  631955.000000            6.319550e+05   \n",
       "mean   2.075238e+07  4.663875e+05       2.360896            9.620796e+05   \n",
       "std    1.899721e+08  6.199704e+06       3.041810            1.705655e+06   \n",
       "min   -1.000000e+00  0.000000e+00       2.000000           -1.000000e+00   \n",
       "25%   -1.000000e+00  0.000000e+00       2.000000            0.000000e+00   \n",
       "50%   -1.000000e+00  0.000000e+00       2.000000            8.761600e+04   \n",
       "75%    1.306116e+06  0.000000e+00       2.000000            3.046400e+05   \n",
       "max    4.431072e+10  8.470000e+08    2269.000000            4.194240e+06   \n",
       "\n",
       "       Init_Win_bytes_backward  RRT_samples_clnt  Act_data_pkt_forward  \\\n",
       "count             6.319550e+05     631955.000000          631955.00000   \n",
       "mean              3.104519e+05          9.733144               6.72471   \n",
       "std               6.647956e+05        347.877923             174.13813   \n",
       "min              -1.000000e+00          0.000000               0.00000   \n",
       "25%              -1.000000e+00          0.000000               1.00000   \n",
       "50%              -1.000000e+00          0.000000               1.00000   \n",
       "75%               9.049600e+04          1.000000               3.00000   \n",
       "max               4.194240e+06      74524.000000           48255.00000   \n",
       "\n",
       "       min_seg_size_forward  \n",
       "count         631955.000000  \n",
       "mean              19.965713  \n",
       "std               14.914261  \n",
       "min                0.000000  \n",
       "25%                0.000000  \n",
       "50%               32.000000  \n",
       "75%               32.000000  \n",
       "max               44.000000  \n",
       "\n",
       "[8 rows x 79 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 631955 entries, 0 to 631954\n",
      "Data columns (total 80 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration                 631955 non-null  int64  \n",
      " 1   total_fpackets           631955 non-null  int64  \n",
      " 2   total_bpackets           631955 non-null  int64  \n",
      " 3   total_fpktl              631955 non-null  int64  \n",
      " 4   total_bpktl              631955 non-null  int64  \n",
      " 5   min_fpktl                631955 non-null  int64  \n",
      " 6   min_bpktl                631955 non-null  int64  \n",
      " 7   max_fpktl                631955 non-null  int64  \n",
      " 8   max_bpktl                631955 non-null  int64  \n",
      " 9   mean_fpktl               631955 non-null  float64\n",
      " 10  mean_bpktl               631955 non-null  float64\n",
      " 11  std_fpktl                631955 non-null  float64\n",
      " 12  std_bpktl                631955 non-null  float64\n",
      " 13  total_fiat               631955 non-null  int64  \n",
      " 14  total_biat               631955 non-null  int64  \n",
      " 15  min_fiat                 631955 non-null  int64  \n",
      " 16  min_biat                 631955 non-null  int64  \n",
      " 17  max_fiat                 631955 non-null  int64  \n",
      " 18  max_biat                 631955 non-null  int64  \n",
      " 19  mean_fiat                631955 non-null  float64\n",
      " 20  mean_biat                631955 non-null  float64\n",
      " 21  std_fiat                 631955 non-null  float64\n",
      " 22  std_biat                 631955 non-null  float64\n",
      " 23  fpsh_cnt                 631955 non-null  int64  \n",
      " 24  bpsh_cnt                 631955 non-null  int64  \n",
      " 25  furg_cnt                 631955 non-null  int64  \n",
      " 26  burg_cnt                 631955 non-null  int64  \n",
      " 27  total_fhlen              631955 non-null  int64  \n",
      " 28  total_bhlen              631955 non-null  int64  \n",
      " 29  fPktsPerSecond           631955 non-null  float64\n",
      " 30  bPktsPerSecond           631955 non-null  float64\n",
      " 31  flowPktsPerSecond        631955 non-null  float64\n",
      " 32  flowBytesPerSecond       631955 non-null  float64\n",
      " 33  min_flowpktl             631955 non-null  int64  \n",
      " 34  max_flowpktl             631955 non-null  int64  \n",
      " 35  mean_flowpktl            631955 non-null  float64\n",
      " 36  std_flowpktl             631955 non-null  float64\n",
      " 37  min_flowiat              631955 non-null  int64  \n",
      " 38  max_flowiat              631955 non-null  int64  \n",
      " 39  mean_flowiat             631955 non-null  float64\n",
      " 40  std_flowiat              631955 non-null  float64\n",
      " 41  flow_fin                 631955 non-null  int64  \n",
      " 42  flow_syn                 631955 non-null  int64  \n",
      " 43  flow_rst                 631955 non-null  int64  \n",
      " 44  flow_psh                 631955 non-null  int64  \n",
      " 45  flow_ack                 631955 non-null  int64  \n",
      " 46  flow_urg                 631955 non-null  int64  \n",
      " 47  flow_cwr                 631955 non-null  int64  \n",
      " 48  flow_ece                 631955 non-null  int64  \n",
      " 49  downUpRatio              631955 non-null  float64\n",
      " 50  avgPacketSize            631955 non-null  float64\n",
      " 51  fAvgSegmentSize          631955 non-null  float64\n",
      " 52  fHeaderBytes             631955 non-null  int64  \n",
      " 53  fAvgBytesPerBulk         631955 non-null  int64  \n",
      " 54  fAvgPacketsPerBulk       631955 non-null  int64  \n",
      " 55  fAvgBulkRate             631955 non-null  int64  \n",
      " 56  bVarianceDataBytes       631955 non-null  float64\n",
      " 57  bAvgSegmentSize          631955 non-null  int64  \n",
      " 58  bAvgBytesPerBulk         631955 non-null  int64  \n",
      " 59  bAvgPacketsPerBulk       631955 non-null  int64  \n",
      " 60  bAvgBulkRate             631955 non-null  int64  \n",
      " 61  sflow_fpacket            631955 non-null  int64  \n",
      " 62  sflow_fbytes             631955 non-null  int64  \n",
      " 63  sflow_bpacket            631955 non-null  int64  \n",
      " 64  sflow_bbytes             631955 non-null  int64  \n",
      " 65  min_active               631955 non-null  int64  \n",
      " 66  mean_active              631955 non-null  float64\n",
      " 67  max_active               631955 non-null  int64  \n",
      " 68  std_active               631955 non-null  float64\n",
      " 69  min_idle                 631955 non-null  int64  \n",
      " 70  mean_idle                631955 non-null  float64\n",
      " 71  max_idle                 631955 non-null  int64  \n",
      " 72  std_idle                 631955 non-null  float64\n",
      " 73  FFNEPD                   631955 non-null  int64  \n",
      " 74  Init_Win_bytes_forward   631955 non-null  int64  \n",
      " 75  Init_Win_bytes_backward  631955 non-null  int64  \n",
      " 76  RRT_samples_clnt         631955 non-null  int64  \n",
      " 77  Act_data_pkt_forward     631955 non-null  int64  \n",
      " 78  min_seg_size_forward     631955 non-null  int64  \n",
      " 79  calss                    631955 non-null  object \n",
      "dtypes: float64(24), int64(55), object(1)\n",
      "memory usage: 385.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calss\n",
       "benign            471597\n",
       "asware            155613\n",
       "GeneralMalware      4745\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['calss'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Extracción de características: PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separamos las variables de entrada (X) de la etiqueta (y)\n",
    "# Transformamos y a valor numérico\n",
    "X_df, y_df = remove_labels(df, 'calss')\n",
    "y_df = y_df.factorize()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La extracción de características es una técnica muy útil para representar un conjunto de datos multidimensional y ganar intuiciones sobre los límites de decisión que construye un algoritmo. Para ello podemos utilizar el algoritmo PCA y reducir el numero de características a 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducimos el conjunto de datos a 2 dimensiones utilizando el algoritmo PCA\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "df_reduced = pca.fit_transform(X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced = pd.DataFrame(df_reduced, columns=[\"c1\", \"c2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.653632e+07</td>\n",
       "      <td>-9.564604e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.704580e+07</td>\n",
       "      <td>-9.898031e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.712784e+07</td>\n",
       "      <td>-9.875840e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.699982e+07</td>\n",
       "      <td>-9.782837e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.702599e+07</td>\n",
       "      <td>-9.829385e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-6.691581e+07</td>\n",
       "      <td>-9.791010e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-6.713106e+07</td>\n",
       "      <td>-9.880533e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-6.709856e+07</td>\n",
       "      <td>-9.884049e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-6.610628e+07</td>\n",
       "      <td>-9.807246e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-6.705952e+07</td>\n",
       "      <td>-9.848264e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             c1            c2\n",
       "0 -6.653632e+07 -9.564604e+06\n",
       "1 -6.704580e+07 -9.898031e+06\n",
       "2 -6.712784e+07 -9.875840e+06\n",
       "3 -6.699982e+07 -9.782837e+06\n",
       "4 -6.702599e+07 -9.829385e+06\n",
       "5 -6.691581e+07 -9.791010e+06\n",
       "6 -6.713106e+07 -9.880533e+06\n",
       "7 -6.709856e+07 -9.884049e+06\n",
       "8 -6.610628e+07 -9.807246e+06\n",
       "9 -6.705952e+07 -9.848264e+06"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representamos el nuevo conjunto de datos con dos características de entrada y tres categorías (y)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df_reduced[\"c1\"][y_df==0], df_reduced[\"c2\"][y_df==0], \"yo\", label=\"normal\")\n",
    "plt.plot(df_reduced[\"c1\"][y_df==1], df_reduced[\"c2\"][y_df==1], \"bs\", label=\"adware\")\n",
    "plt.plot(df_reduced[\"c1\"][y_df==2], df_reduced[\"c2\"][y_df==2], \"g^\", label=\"malware\")\n",
    "plt.xlabel(\"c1\", fontsize=15)\n",
    "plt.ylabel(\"c2\", fontsize=15, rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91695209, 0.05610877])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculamos la proporción de varianza que se ha preservado del conjunto original\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El resultado anterior nos indica que el 91,6% de la varianza del conjunto de datos original se mantiene en el primer eje, y el 5,6% en el segundo eje. Esto quiere decir que aproximadamente 2,8% de la varianza se mantiene en el resto de ejes que no se han utilizado para construir el nuevo conjunto, por lo tanto, es razonable pensar que el resto de características del conjunto de datos no aportaban demasiada información."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representamos el límite de decisión que generaría un algoritmo en este nuevo conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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/1.4/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(max_depth=3, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=42)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generamos un modelo con el conjunto de datos reducido\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf_tree_reduced = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
    "clf_tree_reduced.fit(df_reduced, y_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representamos el límite de decisión generado por el modelo\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, plot_training=True, resolution=1000):\n",
    "    mins = X.min(axis=0) - 1\n",
    "    maxs = X.max(axis=0) + 1\n",
    "    x1, x2 = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "    plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"normal\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"adware\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"malware\")\n",
    "        plt.axis([mins[0], maxs[0], mins[1], maxs[1]])               \n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_decision_boundary(clf_tree_reduced, df_reduced.values, y_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si nuestro objetivo no es visualizar el conjunto de datos, sino reducir la dimensionalidad del conjunto de datos original, en lugar de seleccionar arbitrariamente el numero de dimensiones, sklearn nos proporciona un mecanismo para seleccionar aquellas dimensiones que mantienen un determinado porcetaje de varianza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducimos el conjunto de datos manteniendo el 99,9% de varianza\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=0.999)\n",
    "df_reduced = pca.fit_transform(X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de componentes: 6\n"
     ]
    }
   ],
   "source": [
    "# Numero de dimensionaes del nuevo conjunto\n",
    "print(\"Número de componentes:\", pca.n_components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.16952089e-01, 5.61087653e-02, 2.16566915e-02, 3.65011318e-03,\n",
       "       5.56686331e-04, 3.79356201e-04])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculamos la proporción de varianza que se ha preservado del conjunto original\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
       "      <th>c6</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.653632e+07</td>\n",
       "      <td>-9.564604e+06</td>\n",
       "      <td>3.437284e+06</td>\n",
       "      <td>-2.949219e+06</td>\n",
       "      <td>1.822415e+06</td>\n",
       "      <td>-1.049114e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.704580e+07</td>\n",
       "      <td>-9.898031e+06</td>\n",
       "      <td>3.424601e+06</td>\n",
       "      <td>-3.127607e+06</td>\n",
       "      <td>2.800781e+06</td>\n",
       "      <td>-1.063954e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.712784e+07</td>\n",
       "      <td>-9.875840e+06</td>\n",
       "      <td>3.461085e+06</td>\n",
       "      <td>-3.118886e+06</td>\n",
       "      <td>2.823975e+06</td>\n",
       "      <td>-1.022121e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.699982e+07</td>\n",
       "      <td>-9.782837e+06</td>\n",
       "      <td>3.436564e+06</td>\n",
       "      <td>-3.051254e+06</td>\n",
       "      <td>2.557188e+06</td>\n",
       "      <td>-9.126112e+05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.702599e+07</td>\n",
       "      <td>-9.829385e+06</td>\n",
       "      <td>3.484764e+06</td>\n",
       "      <td>-3.108501e+06</td>\n",
       "      <td>2.726738e+06</td>\n",
       "      <td>-1.074407e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631950</th>\n",
       "      <td>-6.712883e+07</td>\n",
       "      <td>-9.876545e+06</td>\n",
       "      <td>3.460169e+06</td>\n",
       "      <td>-3.121068e+06</td>\n",
       "      <td>2.841346e+06</td>\n",
       "      <td>-1.034425e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631951</th>\n",
       "      <td>-1.490810e+07</td>\n",
       "      <td>1.783457e+07</td>\n",
       "      <td>3.127773e+06</td>\n",
       "      <td>7.128419e+06</td>\n",
       "      <td>-3.846691e+07</td>\n",
       "      <td>-8.013578e+06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631952</th>\n",
       "      <td>2.098170e+07</td>\n",
       "      <td>6.844411e+07</td>\n",
       "      <td>-3.376382e+07</td>\n",
       "      <td>-2.849601e+07</td>\n",
       "      <td>2.306222e+06</td>\n",
       "      <td>-3.567339e+05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631953</th>\n",
       "      <td>1.163357e+07</td>\n",
       "      <td>3.237585e+07</td>\n",
       "      <td>5.077168e+06</td>\n",
       "      <td>1.323917e+07</td>\n",
       "      <td>-5.531286e+07</td>\n",
       "      <td>-1.294719e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631954</th>\n",
       "      <td>-1.486282e+07</td>\n",
       "      <td>1.778576e+07</td>\n",
       "      <td>3.238090e+06</td>\n",
       "      <td>7.021148e+06</td>\n",
       "      <td>-3.809729e+07</td>\n",
       "      <td>-8.235398e+06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>631955 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  c1            c2            c3            c4            c5  \\\n",
       "0      -6.653632e+07 -9.564604e+06  3.437284e+06 -2.949219e+06  1.822415e+06   \n",
       "1      -6.704580e+07 -9.898031e+06  3.424601e+06 -3.127607e+06  2.800781e+06   \n",
       "2      -6.712784e+07 -9.875840e+06  3.461085e+06 -3.118886e+06  2.823975e+06   \n",
       "3      -6.699982e+07 -9.782837e+06  3.436564e+06 -3.051254e+06  2.557188e+06   \n",
       "4      -6.702599e+07 -9.829385e+06  3.484764e+06 -3.108501e+06  2.726738e+06   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "631950 -6.712883e+07 -9.876545e+06  3.460169e+06 -3.121068e+06  2.841346e+06   \n",
       "631951 -1.490810e+07  1.783457e+07  3.127773e+06  7.128419e+06 -3.846691e+07   \n",
       "631952  2.098170e+07  6.844411e+07 -3.376382e+07 -2.849601e+07  2.306222e+06   \n",
       "631953  1.163357e+07  3.237585e+07  5.077168e+06  1.323917e+07 -5.531286e+07   \n",
       "631954 -1.486282e+07  1.778576e+07  3.238090e+06  7.021148e+06 -3.809729e+07   \n",
       "\n",
       "                  c6  Class  \n",
       "0      -1.049114e+06      0  \n",
       "1      -1.063954e+06      0  \n",
       "2      -1.022121e+06      0  \n",
       "3      -9.126112e+05      0  \n",
       "4      -1.074407e+06      0  \n",
       "...              ...    ...  \n",
       "631950 -1.034425e+06      0  \n",
       "631951 -8.013578e+06      2  \n",
       "631952 -3.567339e+05      1  \n",
       "631953 -1.294719e+07      0  \n",
       "631954 -8.235398e+06      2  \n",
       "\n",
       "[631955 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformamos a un DataFrame de Pandas\n",
    "df_reduced = pd.DataFrame(df_reduced, columns=[\"c1\", \"c2\", \"c3\", \"c4\", \"c5\", \"c6\"])\n",
    "df_reduced[\"Class\"] = y_df\n",
    "df_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. División del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividimos el conjunto de datos\n",
    "train_set, val_set, test_set = train_val_test_split(df_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = remove_labels(train_set, 'Class')\n",
    "X_val, y_val = remove_labels(val_set, 'Class')\n",
    "X_test, y_test = remove_labels(test_set, 'Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=30, n_estimators=200, n_jobs=-1,\n",
       "                       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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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/1.4/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(max_depth=30, n_estimators=200, n_jobs=-1,\n",
       "                       random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=30, n_estimators=200, n_jobs=-1,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf_rnd = RandomForestClassifier(n_estimators=200, max_depth=30, random_state=42, n_jobs=-1)\n",
    "clf_rnd.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predecimos con el conjunto de datos de validación\n",
    "y_val_pred = clf_rnd.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score validation test: 0.8914140064148489\n"
     ]
    }
   ],
   "source": [
    "# F1 score conjunto de datos de validación\n",
    "print(\"F1 score validation test:\", f1_score(y_val_pred, y_val, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predecimos con el conjunto de datos de pruebas\n",
    "y_test_pred = clf_rnd.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score test set: 0.8945365648002198\n"
     ]
    }
   ],
   "source": [
    "# F1 score conjunto de datos de pruebas\n",
    "print(\"F1 score test set:\", f1_score(y_test_pred, y_test, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}