上次碰到单一决策树预测效果不好的情况，实际预测中，对于不同的特征值，应该要赋予不同的权重，正向激励能够生成指定数量的新决策树，每个数都在上一个数预测的结果上，根据预测值跟实际值的偏差，进行权重调整，达到提高预测期望的结果
import sklearn.model_selection as msimport sklearn.datasets as sdimport sklearn.tree as stimport sklearn.metrics as smimport sklearn.ensemble as seboston = sd.load_boston()train_x, test_x, train_y, test_y = ms.train_test_split(boston.data, boston.target, test_size=0.1, random_state=7) model = st.DecisionTreeRegressor(max_depth=6)model = se.AdaBoostRegressor(model,n_estimators=700,random_state=7)model.fit(train_x,train_y)pred_test_y = model.predict(test_x)pred_train_y = model.predict(train_x)print('训练集:',sm.r2_score(train_y,pred_train_y))print('测试集:',sm.r2_score(test_y,pred_test_y))
结果
用到两个模块
import sklearn.tree as st
构建一个树
import sklearn.ensemble as se
构建多个树，不停迭代调参
训练集: 0.9819587536039245测试集: 0.9209163745104365
