Day 11_K-NN.md

K近邻法 (K-NN)

数据集 | 社交网络

导入相关库

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

导入数据集

dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

将数据划分成训练集和测试集

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

特征缩放

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

使用K-NN对训练集数据进行训练

from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

对测试集进行预测

y_pred = classifier.predict(X_test)

生成混淆矩阵

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)