{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Задача 12. \n", "\n", "Предсказать сорт винограда из которого сделано вино, используя результаты химических анализов, c помощью KNN - метода k ближайших соседей с тремя различными метриками. Построить график зависимости величины ошибки от числа соседей k." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn import model_selection\n", "from sklearn import neighbors\n", "from sklearn import metrics\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Установим реккомендуемые параметры:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['font.family'] = 'serif'\n", "plt.rcParams['font.serif'] = 'FreeSerif'\n", "plt.rcParams['lines.linewidth'] = 2\n", "plt.rcParams['lines.markersize'] = 12\n", "plt.rcParams['xtick.labelsize'] = 24\n", "plt.rcParams['ytick.labelsize'] = 24\n", "plt.rcParams['legend.fontsize'] = 24\n", "plt.rcParams['axes.titlesize'] = 36\n", "plt.rcParams['axes.labelsize'] = 24" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузим данные:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Class | \n", "Alcohol | \n", "Malic acid | \n", "Ash | \n", "Alcalinity of ash | \n", "Magnesium | \n", "Total phenols | \n", "Flavanoids | \n", "Nonflavanoid phenols | \n", "Proanthocyanins | \n", "Color intensity | \n", "Hue | \n", "OD280/OD315 of diluted wines | \n", "Proline | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "1 | \n", "14.23 | \n", "1.71 | \n", "2.43 | \n", "15.6 | \n", "127 | \n", "2.80 | \n", "3.06 | \n", "0.28 | \n", "2.29 | \n", "5.64 | \n", "1.04 | \n", "3.92 | \n", "1065 | \n", "
1 | \n", "1 | \n", "13.20 | \n", "1.78 | \n", "2.14 | \n", "11.2 | \n", "100 | \n", "2.65 | \n", "2.76 | \n", "0.26 | \n", "1.28 | \n", "4.38 | \n", "1.05 | \n", "3.40 | \n", "1050 | \n", "
2 | \n", "1 | \n", "13.16 | \n", "2.36 | \n", "2.67 | \n", "18.6 | \n", "101 | \n", "2.80 | \n", "3.24 | \n", "0.30 | \n", "2.81 | \n", "5.68 | \n", "1.03 | \n", "3.17 | \n", "1185 | \n", "
3 | \n", "1 | \n", "14.37 | \n", "1.95 | \n", "2.50 | \n", "16.8 | \n", "113 | \n", "3.85 | \n", "3.49 | \n", "0.24 | \n", "2.18 | \n", "7.80 | \n", "0.86 | \n", "3.45 | \n", "1480 | \n", "
4 | \n", "1 | \n", "13.24 | \n", "2.59 | \n", "2.87 | \n", "21.0 | \n", "118 | \n", "2.80 | \n", "2.69 | \n", "0.39 | \n", "1.82 | \n", "4.32 | \n", "1.04 | \n", "2.93 | \n", "735 | \n", "