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+# 决策树分类
+<p align="center">
+  <img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/raw/master/Info-graphs/Day%2023%20-%20Chinese.jpg">
+</p>
+
+https://github.com/MachineLearning100/100-Days-Of-ML-Code/raw/master/Info-graphs/Day%2023%20-%20Chinese.jpg
+### 导入需要用到的python库
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+```
+
+### 导入数据集
+```python
+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
+```python
+from sklearn.cross_validation 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
+```python
+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
+```python
+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
+```python
+y_pred = classifier.predict(X_test)
+```
+### 制作混淆矩阵
+```python
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+```
+### 将训练集结果进行可视化
+```python
+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, stop = X_set[:, 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
+```python
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+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, stop = X_set[:, 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 (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+```