DecisionTreeRegressor

1.重要参数criterion

        mse:

                在回归树中，mse不仅是分支质量衡量标准，也是我们常用的衡量回归树回归质量的衡量指标，但是回归树接口返回的时R平方，并不是mse。 

2.（扩展）交叉验证--多次划分训练集，测试集多次计算模型的稳定性

from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeRegressor

boston = load_boston()
regressor = DecisionTreeRegressor(random_state=0)
cross_val_score(regressor
               ,boston.data
               ,boston.target
               ,cv=10
               ,scoring='neg_mean_squared_error'

 3.一维回归曲线绘制

        1.导库

import numpy as np
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pylot as plt

        2.创建一条含有噪音的正弦曲线

rng=np.random.RandomState(1)    #生成随机数种子
rng.rand(80，1)    #生成8行10列个0，1之间随机数，特征必须是二维的
X = np.sort(5*rng.rand(80,1),axis=0)
y = np.sin(X).ravel()    #标签必须是一维
y[::5] += 3*(0.5 - rng.rand(16))    #转化为-0.5，0.5之间

        3.实例化，训练模型

regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X, y)
regr_2.fit(X, y)

         4.生成测试集，导入模型

x_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis] #生成（起始点，终止点，步长）；升维度
y_1 = regr_1.predict(X_test)
y_2 = regr_2.predict(X_test)

         5.绘图

plt.figure()
plt.scatter(X, y, s=20, edgecolor="black",c="darkorange", label="data")
plt.plot(X_test, y_1, color="cornflowerblue",label="max_depth=2", linewidth=2)
plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Decision Tree Regression")
plt.legend()
plt.show()

4.(扩展)用网格搜索调整参数

import numpy as np
from sklearn.model_selection  import GridSearchCV
gini_thresholds = np.linspace(0,0.5,20)

parameters = {
            "splitter":("best","random")
            ,"criterion":("gini","entropy")
            ,"max_depth":[*range(1,10)]
            ,"min_samples_leaf":[*range(1,50,5)]
            ,"min_impurity_decrease":[*np.linspace(0,0.5,20)]
            }
clf = DecisionTreeRegressor(random_state=20)
GS  = GridSearchCV(clf, parameters, cv=10)
GS.fit(Xtrain,Ytrain)

GS.best_params_
GS.best_score_

5.总结：决策树的优缺点