callback=[LGB.early_stopping(stopping_rounds=10,verbose=True), LGB.log_evaluation(period=10,show_stdv=True)] # 训练 train m1 = LGB.train(params,lgb_train,num_boost_round=2000, valid_sets=[lgb_train,lgb_eval],callbacks=callback) #预测数据集 y_pred = m1.predict(X_test) #评估模型 reg...
Each column represents one model; for example, Model (1) has an outcome of sense of belonging in the respondent’s neighborhood and includes only male respondents. For ease of interpretation, log odds are presented. Numbers in parentheses in the Variables column indicate the level for which the...
[final-1]) , ha='center', va='bottom', fontsize=10) plt.text(final, metric_dict['valid_0']['binary_logloss'][final-1] ,"final:"+str(metric_dict['valid_0']['binary_logloss'][final-1]) , ha='center', va='bottom', fontsize=10) plt.show() #样例 #picture_lgb_loss(...
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. - LightGBM/R-package/R/lgb.cv.R at master · micro