) model=lgb_model.fit(train_x,train_dev['imp'], eval_set=[(test_x,dev['imp'])], early_stopping_rounds=10000,eval_metric=eval_f,verbose=100) 其中,评价标准部分自定义了评估函数: def eval_f(y_true,y_pred): global best_score global test_cont y_pred=np.maximum(np.exp(y_pred*test...
eval_set: list or None, optional (default=None) 使用(X,y) 元组作为验证集 eval_names: list of strings or None, optional (default=None) 验证集的名称 eval_sample_weight: list of arrays or None, optional (default=None) 验证集的权重(回归模型) eval_class_weight: list or None, optional (d...
eval_set: list or None, optional (default=None) 使用(X,y) 元组作为验证集 eval_names: list of strings or None, optional (default=None) 验证集的名称 eval_sample_weight: list of arrays or None, optional (default=None) 验证集的权重(回归模型) eval_class_weight: list or None, optional (d...
1e-4,1.0),'n_estimators':trial.suggest_int('n_estimators',20,200),'max_depth':trial.suggest_int('max_depth',-1,15),}model=lgb.LGBMRegressor(**param)model.fit(X_train,y_train,eval
eval_metric: 评价指标,可以⽤lgb⾃带的,也可以⾃定义评价函数,# 如下,评价函数为l1,程序会⾃动将预测值和标签传⼊eval_metric中,并返回score gbm = lgb.LGBMRegressor(num_leaves=31,learning_rate=0.05,n_estimators=20)gbm.fit(X_train, y_train,eval_set=[(X_test, y_test)],eval_metric=...
eval_set: list or None, optional (default=None) 使用(X,y) 元组作为验证集 eval_names: list of strings or None, optional (default=None) 验证集的名称 eval_sample_weight: list of arrays or None, optional (default=None) 验证集的权重(回归模型) ...
eval_set=[(X_train, y_train), (X_valid, y_valid)], eval_metric='binary_logloss', verbose=50, early_stopping_rounds=200) y_pred_valid = model.predict(X_valid) y_pred = model.predict(X_test, num_iteration=model.best_iteration_) ...
# sklearn 接口 gbm = LGBMClassifier(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.05, n_estimators=20) gbm.fit(X_train, y_train, eval_set=(X_test, y_test), eval_metric='multi_logloss') [1] valid_0's multi_logloss: 1.4861 [2] valid_0's multi_logloss...
然后直接去统计每个group内的samples的数量，传入这个数量数组即可。eval_group设置同理。另外，推断预测...
format(fold_id+1)) lgb_model = model.fit(X_train, Y_train, eval_names=['train', 'valid'], eval_set=[(X_train, Y_train), (X_val, Y_val)], verbose=500, eval_metric='auc', early_stopping_rounds=50) pred_val = lgb_model.predict_proba( X_val, num_iteration=lgb_model.best...