{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo Decision Tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### El objetivo es clasificar con un Decision Tree los datos obtenidos a partir de una simulación no lineal que se encuentran en el fichero \"ejemplo_dataset\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cargamos las librerías necesarias" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Tratamiento de datos\n", "# ==============================================================================\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Gráficos\n", "# ==============================================================================\n", "import matplotlib.pyplot as plt\n", "from matplotlib import style\n", "import seaborn as sns\n", "\n", "# Preprocesado y modelado\n", "# ==============================================================================\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import train_test_split\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creamos y visualizamos el set de datos" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | X1 | \n", "X2 | \n", "y | \n", "
---|---|---|---|
0 | \n", "2.526093 | \n", "0.321050 | \n", "0 | \n", "
1 | \n", "0.366954 | \n", "0.031462 | \n", "0 | \n", "
2 | \n", "0.768219 | \n", "0.717486 | \n", "0 | \n", "
3 | \n", "0.693436 | \n", "0.777194 | \n", "0 | \n", "
4 | \n", "-0.019837 | \n", "0.867254 | \n", "0 | \n", "