def feature_importances_(self): """ Feature importances property .. note:: Feature importance is defined only for tree boosters Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such...
def feature_importances_(self): """ Feature importances property .. note:: Feature importance is defined only for tree boosters Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such...
Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now. ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if self._n_features is...
pred_oof) == scores[-1]assertroc_auc_score(y_test, pred_test) >=0.85# test roc_aucassertroc_auc_score(y, models[0].predict_proba(X)[:,1]) >=0.85# make sure models are trainedassertlen(importance) ==5assertlist(importance[0].columns) == ['feature','importance...
from lightgbm import plot_importance import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载样本数据集 iris = load_iris() X,y = iris.data,iris.target X_train,X_test,y_train,y_test = train_test_split(X,y,test...
assert importance_df["feature"].values[0] == "B", "Most important feature is different than B!" Example #9Source File: test_lightgbm.py From m2cgen with MIT License 6 votes def test_multi_class(): estimator = lightgbm.LGBMClassifier(n_estimators=1, random_state=1, max_depth=1) ...
importance_type: string, optional (default="split"). How the importance is calculated. 字符串,可选(默认值=“split”)。如何计算重要性。 If "split", result contains numbers of times the feature is used in a model. 如果“split”,则结果包含该特征在模型中使用的次数。
importance_type : string, optional (default="split"). How the importance is calculated. 字符串,可选(默认值=“split”)。如何计算重要性。 If "split", result contains numbers of times the feature is used in a model. 如果“split”,则结果包含该特征在模型中使用的次数。
ML:LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略 ML:LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法精解之详细攻略LG LGBMClassifier XGBClassifier CatBoostClassif feature_importa ...
("min_gain_to_split",0.0,1.0), #"reg_alpha": hp.uniform("reg_alpha", 0, 2), #"reg_lambda": hp.uniform("reg_lambda", 0, 2), #"feature_fraction":hp.uniform("feature_fraction",0.5,1.0), #"bagging_fraction":hp.uniform("bagging_fraction",0.5,1.0), #"bagging_freq":hp.choice(...