Day_5 K近鄰法
阿新 • • 發佈:2019-01-02
匯入相關庫
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
匯入資料集
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
將資料劃分成訓練集和測試集
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)
特徵縮放
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
使用K-NN對訓練集資料進行訓練
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)