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...
和决策树完全一致,除了多了参数n_estimators。 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, bo...
随机森林回归器 RandomForestRegressor 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,boo...
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor # 分类树 data_wine = load_wine() # 加载红酒数据集 # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data_wine.data, data_wine.target, test_size=0.3, random_state=42) rfc = RandomForestC...
sklearn.ensemble.RandomForestRegressor参数含义 RandomForestRegressor n_estimators:int, default=100。森林中树木的数量。 max_depth:int, default=None。树的最大深度。如果为None,则将节点展开,直到所有叶子都是纯净的,或者直到所有叶子都包含少于min_samples_split个样本。
classsklearn.ensemble.RandomForestClassifier(n_estimators=100,criterion='gini',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_jobs=None...
class sklearn.ensemble.RandomForestRegressor(n_estimators=’warn’, 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_sc...
ensemble import RandomForestRegressor from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV # Attention! Data Partition # Attention! One-Hot Encoding train_data_path='G:/CropYield/03_DL/00_Data/AllDataAll_Train.csv' test_data_path='G:/CropYield/...
sklearn.ensemble：提供集成学习方法，如随机森林（RandomForestClassifier/Regressor）、梯度提升树（GradientBoostingClassifier/Regressor）等。集成学习通过组合多个弱学习器来提高模型的性能。无监督学习模块 无监督学习是机器学习中的另一大类方法，它主要用于处理无标签数据，发现数据中的结构和关系。sklearn.cluster：提供...
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