前言
本節(jié)進行knn算法的實驗部分。
前情回顧： K近鄰法（K-NN，k-NearestNeighbor）
Step 0：數(shù)據(jù)準(zhǔn)備
User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
... ...
Step 1：數(shù)據(jù)集處理
導(dǎo)入相關(guān)庫
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
導(dǎo)入數(shù)據(jù)集
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
劃分訓(xùn)練集和測試集
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)
特征縮放
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
Step2：訓(xùn)練預(yù)測
使用K-NN對訓(xùn)練集數(shù)據(jù)進行訓(xùn)練
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
對測試集進行預(yù)測
y_pred = classifier.predict(X_test)
生成混淆矩陣
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
