classsklearn.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs...
classsklearn.ensemble.RandomForestRegressor(n_estimators=100,*,criterion='mse', max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0, max_features='auto',max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,bootstrap=True,oob_score=False,n_job...
fromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimportcross_val_scorefromsklearn.ensembleimportRandomForestRegressor boston=load_boston() regressor= RandomForestRegressor(n_estimators=100,random_state=0) cross_val_score(regressor, boston.data, boston.target, cv=10,scoring="neg_mean_square...
class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score...
如果希望用袋外数据来测试,则需要在实例化时就将oob_score这个参数调整为True,训练完毕之后,用随机森林的另一个重要属性:oob_score_来查看袋外数据上测试的结果: #无需划分训练集和测试集rfc=RandomForestClassifier(n_estimators=25,oob_score=True)rfc=rfc.fit(wine.data,wine.target)#重要属性oob_score_rfc....
,"Random Forest:{}".format(score_r) ) 4. 画出随机森林和决策树在一组交叉验证下的效果对比 #目的是带大家复习一下交叉验证#交叉验证:是数据集划分为n分,依次取每一份做测试集,每n-1份做训练集,多次训练模型以观测模型稳定性的方法fromsklearn.model_selectionimportcross_val_scoreimportmatplotlib.pyplot ...
reg = RandomForestRegressor() # 回归树 reg.fit(X_train, y_train) # 拟合训练集 print(reg.predict(X_train)) # 测试集的预测结果 print(reg.score(X_test, y_test)) # 测试集上的决定系数R2 常用属性和接口 .feature_importances_:每个特征的特征重要性,总和为1 ...
X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3)# Fit a base modelforest = RandomForestRegressor()_ = forest.fit(X_train, y_train)>>> print(f"R2 for training set: {forest.score(X_train, y_train)}")>>> print(f"R2 for validation set: {forest.score(X_...
3、RandomForestRegressor重要参数：criterion：string, optional (default=”mse”)1、输入“mse”使用均方误差mean squared err(MSE)，父节点和子节点之间的均方误差的差额将被用来作为特征选择的标准，这种方法通过使用叶子节点的均值来最小化L2损失。2、输入“friedman_mse”使用费尔德曼均方误差，这种指标...
RandomForestRegressor(bootstrap = True,criterion="mse", splitter="best",max_depth=None, max_features='auto',max_leaf_nodes=None, min_impurity_decrease=0.,min_impurity_split=None, min_samples_leaf=1,min_samples_split=2, min_weight_fraction_leaf=0.,n_estimators = 10, ...