📷“‘’从sklearn.ensemble import RandomForestClassifier forest = RandomForestClassifier(n_estimators = 500,max_depth = None,min_samples_split=2,min_samples_leaf =1,bootstrap = True,random_state=0) max_depth= forest.fit(X_train,y_train) print(forest.score(X_test,y_test))‘” 浏览0...
estimators = [("Tree", DecisionTreeRegressor()), ("RandomForestRegressor", RandomForestRegressor(random_state=100)), ("ExtraTreesClassifier", ExtraTreesRegressor(random_state=100)), ] n_estimators = len(estimators) # Generate data def f(x): x = x.ravel() return np.exp(-x ** 2) + ...
random_state:用来设置分枝中的随机模式的参数,默认为None; bootarap:代表采用有放回的随机抽样技术; obb_score:袋外数据测试,将obb_score参数调整为True,训练完毕后,用obb_score_属性来查看袋外数据上测试的结果; 随机森林四个常用接口:apply;fit;predict;score 2. 随机森林回归 RandomForestRegressor 与分类基本差...
ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score scores =[] for k in range(1, 200): rfc = RandomForestClassifier(n_estimators=k) rfc.fit(x_train, y_train) y_pred = rfc.predict(x_test) scores.append(accuracy_score(y_test, y_pred)) import matplotlib.pyplo...
FontTian 0 466 随机森林 2019-08-21 13:44 −from sklearn.ensemble import RandomForestRegressor #导入随机森林的包 import pandas as pd #加载入数据,这里用的是住房的数据 from sklearn.datasets.california_housing impor... admin9s 0 199 <123>...
@raghavrvI'm not so sure about these methods having a "_best_estimator" which is used. This kind of makes sense when you can have an ensemble of estimators with different hyperparameters, but with trees the idea is that since you're building them iteratively there is only one forest at ...
1 RandomForest, how to choose the optimal n_estimator parameter 5 Hyperparameter Tuning in Random forest 0 RandomForestRegressor for classification problems 1 Random forest getting mse by tuning two hyperparameters using a for loop 1 Python optimization of prediction of random forest regressor ...
1 提升集成算法:重要参数n_estimators 1. 导入需要的库,模块以及数据 from xgboost import XGBRegressor as XGBR from sklearn.ensemble import RandomForestRegressor as RFR from skl
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因此,我找到了一个scikit-learn API for XGBoost random forest regression,并使用X特征和全零的Y数据集做了一个小的SW测试。 from numpy import array from xgboost import XGBRFRegressor from sklearn.ensemble import RandomForestRegressor tree_number = 100 d 浏览41提问于2021-04-16得票数 8 回答已采纳...