利用RandomForestRegressor训练模型

from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)
rfr_y_predict = rfr.predict(X_test)

性能评测

print 'R-squared value of RandomForestRegressor:', rfr.score(X_test, y_test)
print 'The mean squared error of RandomForestRegressor:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict))
print 'The mean absoluate error of RandomForestRegressor:', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict))

使用ExtraTreesRegressor训练模型

etr = ExtraTreesRegressor()
etr.fit(X_train, y_train)
etr_y_predict = etr.predict(X_test)

性能测评

print 'R-squared value of ExtraTreesRegessor:', etr.score(X_test, y_test)
print 'The mean squared error of  ExtraTreesRegessor:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(etr_y_predict))
print 'The mean absoluate error of ExtraTreesRegessor:', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(etr_y_predict))

print np.sort(zip(etr.feature_importances_, boston.feature_names), axis=0)

使用GradientBoostingRegressor训练模型

gbr = GradientBoostingRegressor()
gbr.fit(X_train, y_train)
gbr_y_predict = gbr.predict(X_test)

性能测评

print 'R-squared value of GradientBoostingRegressor:', gbr.score(X_test, y_test)
print 'The mean squared error of GradientBoostingRegressor:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(gbr_y_predict))
print 'The mean absoluate error of GradientBoostingRegressor:', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(gbr_y_predict))