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算法部分不再细讲，之前发过很多：
【算法系列】决策树
决策树（Decision Tree）ID3算法
决策树（Decision Tree）C4.5算法
决策树（Decision Tree）CART算法
ID3、C4.5、CART三种决策树的区别
实验：
导入需要用到的python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
将数据集拆分为训练集和测试集
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)
对测试集进行决策树分类拟合
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy',random_state = 0)
classifier.fit(X_train,y_train)
预测测试集的结果
y_pred = classifier.predict(X_test)
制作混淆矩阵
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)
将训练集结果进行可视化
from matplotlib.colors import ListedColormap
X_set,y_set = X_train,y_train
X1,X2 = np.meshgrid(np.arange(start = X_set[:,0].min() - 1,stop = X_set[:,0].max() + 1,step = 0.01),np.arange(start = X_set[:,1].min() - 1,1].max() + 1,step = 0.01))
plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75,cmap = ListedColormap(('red','green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j,0],X_set[y_set == j,1],c = ListedColormap(('red','green'))(i),label = j)
plt.title('Decision Tree Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
将测试集结果进行可视化
from matplotlib.colors import ListedColormap
X_set,y_set = X_test,y_test
X1,label = j)
plt.title('Decision Tree Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
（编辑：北几岛）
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