决策树是一种用于分类和回归的非参数监督学习方法。目标是创建一个模型，通过从数据特性中推导出简单的决策规则来预测目标变量的值
1 import numpy as np
2 import pandas as pd
3 from sklearn.feature_extraction import DictVectorizer
4 from sklearn.tree import DecisionTreeClassifier
5 from sklearn.model_selection import train_test_split 1 def decide_play1():
2 df = pd.read_csv('dtree.csv')
3 dict_train = df.to_dict(orient='record')
4
5 dv = DictVectorizer(sparse=False)
6 dv_train = dv.fit_transform(dict_train)
7 # print(dv_train)
8 # dv_train1 = np.append(dv_train, dv_train[:, 5].reshape(-1, 1), axis=1)
9 # dv_train2 = np.delete(dv_train1, 5, axis=1)
10 # print('*' * 50)
11 # print(dv_train2)
12
13 # print(dv_train[:,:5])
14 # print(dv_train[:,6:])
15 # print(dv_train[:,5])
16 y = dv_train[:, 5]
17 x = np.delete(dv_train, 5, axis=1)
18 print(x)
19 print(y)
20 dtc = DecisionTreeClassifier()
21 dtc.fit(x, y.reshape(-1, 1))
22 print(dtc.predict(np.array([x[3]]))) 1 def decide_play():
2 # ID3
3 df = pd.read_csv('dtree.csv')
4 # 将数据转换为字典格式，orient='record'参数指定数据格式为{column:value,column:value}的形式
5 dict_train = df.loc[:, ['Outlook', 'Temperatur', 'Humidity', 'Windy']].to_dict(orient='record')
6 dict_target = pd.DataFrame(df['PlayGolf'], columns=['PlayGolf']).to_dict(orient='record')
7
8
9 # 训练数据字典向量化
10 dv_train = DictVectorizer(sparse=False)
11 x_train = dv_train.fit_transform(dict_train)
12
13 # 目标数据字典向量化
14 dv_target = DictVectorizer(sparse=False)
15 y_target = dv_target.fit_transform(dict_target)
16
17 # 创建训练模型并训练
18 d_tree = DecisionTreeClassifier()
19 d_tree.fit(x_train, y_target)
20
21 data_predict = {
22 'Humidity': 85,
23 'Outlook': 'sunny',
24 'Temperatur': 85,
25 'Windy': False
26 }
27
28 x_data = dv_train.transform(data_predict)
29 print(dv_target.inverse_transform(d_tree.predict(x_data)))
30
31
32 if __name__ == '__main__':
33 decide_play() 1 import numpy as np
2 import pandas as pd
3 from sklearn.feature_extraction import DictVectorizer
4 from sklearn.model_selection import train_test_split
5 from sklearn.tree import DecisionTreeClassifier
6 from sklearn.metrics import r2_score
7
8
9 def titanic_tree():
10 # 获取数据
11 df = pd.read_csv('Titanic.csv')
12 # df = df.fillna(0)
13 # dict_train = df.loc[:, ['Pclass', 'Age', 'Sex']].to_dict(orient='record')
14 # dict_target = pd.DataFrame(df['Survived'], columns=['Survived']).to_dict(orient='record')
15 # x_train, x_test, y_train, y_test = train_test_split(dict_train, dict_target, test_size=0.25)
16
17 # 处理数据，找出特征值和目标值
18 x = df.loc[:, ['Pclass', 'Age', 'Sex']]
19 y = df.loc[:, ['Survived']]
20 # 缺失值处理
21 x['Age'].fillna(x['Age'].mean(), inplace=True)
22 # 分割数据集到训练集和测试集
23 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
24 # print(y_test)
25 dv_train = DictVectorizer(sparse=False)
26 x_train = dv_train.fit_transform(x_train.to_dict(orient='record'))
27 x_test = dv_train.transform(x_test.to_dict(orient='record'))
28
29 dv_target = DictVectorizer(sparse=False)
30 y_target = dv_target.fit_transform(y_train.to_dict(orient='record'))
31 y_test = dv_target.transform(y_test.to_dict(orient='record'))
32 # print(y_test)
33 # 用决策树进行预测
34 d_tree = DecisionTreeClassifier()
35 d_tree.fit(x_train, y_train)
36
37 data_predict = {
38 'Pclass': 1,
39 'Age': 38,
40 'Sex': 'female'
41
42 }
43
44 x_data = dv_train.transform(data_predict)
45 print(dv_target.inverse_transform(d_tree.predict(x_data).reshape(-1,1)))
46 # print(d_tree.predict(x_test))
47 # print(y_test)
48 # 预测准确率
49 # print(d_tree.score(x_test, y_test))
50
51
52 if __name__ == '__main__':
53 titanic_tree()(Decision Tree)及其变种是另一类将输入空间分成不同的区域，每个区域有独立参数的算法。
决策树分类算法是一种基于实例的归纳学习方法，它能从给定的无序的训练样本中，提炼出树型的分类模型。树中的每个非叶子节点记录了使用哪个特征来进行类别的判断，每个叶子节点则代表了最后判断的类别。根节点到每个叶子节点均形成一条分类的路径规则。而对新的样本进行测试时，只需要从根节点开始，在每个分支节点进行测试，沿着相应的分支递归地进入子树再测试，一直到达叶子节点，该叶子节点所代表的类别即是当前测试样本的预测类别
