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...
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 as linear learners .仅当选择决策树模型作为基础学习者(`booster=gbtree`)时,才定义特征重要性。它不适用于其他基本学习者类型,例如线...
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...
# 需要导入模块: import lightgbm [as 别名]# 或者: from lightgbm importLGBMRegressor[as 别名]defget_feature_importances(data, shuffle, cats=[], seed=None):# Gather real featurestrain_features = [fforfindataiffnotin[target] + cols2ignore]# Shuffle target if requiredy = data[target].copy()...
(params=lgb_params, train_set=dtrain, num_boost_round=20) # Get feature importances imp_df = pd.DataFrame() imp_df["feature"] = x_train.columns imp_df["importance_gain"] = clf.feature_importance(importance_type='gain') imp_df["importance_split"] = clf.feature_importance(importance_...
print(repr(lgb_train.feature_name[6])) # 存储模型 gbm.save_model('../../tmp/lgb_model.txt') #特征名称print('特征名称:') print(gbm.feature_name()) #特征重要度print('特征重要度:') print(list(gbm.feature_importance())) # 加载模型 ...
Recursive Feature Elimination 给定一个为权重(例如线性模型的系数)分配权重的外部估计器,递归特征消除(RFE)的目标是通过递归考虑越来越少的特征集来选择特征。首先,对估计器进行初始特征集的训练,并且通过coef_属性或feature_importances_属性获得每个特征的重要性。
importances = clf.feature_importances_ features = X.columns #accuracy is calculated each fold so divide by n_folds. #not n_folds -1 because it is not sum by row but overall sum of accuracy of all test indices # 这里不是n_folds-1,是因为不是按行求和,而是按指标求和。
logger.info(f'feature importance: {pair_list}') return pair_list def valadation(url,feature_list): X_train, Y_train, X_test, Y_test = get_train_test_data(url, feature_list) lgbm_model = get_model() lgbm_model = trian_test(lgbm_model, X_train, Y_train) ...
def feature_importances(self, x, y): return self.clf.fit(x, y).feature_importances_ def get_oof(clf, x_train, y_train, x_test): oof_train = np.zeros((ntrain,)) oof_test = np.zeros((ntest,)) oof_test_skf = np.empty((NFOLDS, ntest)) ...