{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ejemplo SVC Lineal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### El objetivo es clasificar con un modelo SVC lineal 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": 37,
"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.svm import SVC\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": 43,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" X1 | \n",
" X2 | \n",
" y | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 2.526093 | \n",
" 0.321050 | \n",
" 0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.366954 | \n",
" 0.031462 | \n",
" 0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.768219 | \n",
" 0.717486 | \n",
" 0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.693436 | \n",
" 0.777194 | \n",
" 0 | \n",
"
\n",
" \n",
" | 4 | \n",
" -0.019837 | \n",
" 0.867254 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"