决策树回归

2016-07-13 机器学习 PV:

决策树解决回归问题

1 2	import numpy as np import matplotlib.pyplot as plt

from sklearn import datasets

boston = datasets.load_boston()
X = boston.data
y = boston.target

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from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

Decision Tree Regressor

from sklearn.tree import DecisionTreeRegressor

dt_reg = DecisionTreeRegressor()
dt_reg.fit(X_train, y_train)

DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
           max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           presort=False, random_state=None, splitter='best')

1	dt_reg.score(X_test, y_test)

0.58605479243964098

1	dt_reg.score(X_train, y_train)

1.0