super(LGBMClassifier, self).fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, early_stopping_rounds...
eval_set:用于早停法的验证集。 eval_metric:评估指标。 early_stopping_rounds:早停法的轮数,用于在验证集上性能不再提升时提前停止训练。 fit方法的完整调用方式可能如下: python model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric='logloss', early_stopping_rounds=10) 确认...
elif eval_metric in ('error', 'multi_error'): eval_metric = 'binary_error' if eval_set is not None: if isinstance(eval_set, tuple): eval_set = [eval_set] for i, (valid_x, valid_y) in enumerate(eval_set): if valid_x is X and valid_y is y: eval_set[i] = valid_x, _...
y, # array, Series 类型 eval_set=None, # 用于评估的数据集,例如:[(X_train, y_train), (X_test, y_test)] eval_metric=None, # 评估函数,字符串类型,例如:'l2', 'logloss' early_stopping_rounds=None, verbose=True # 设置为正整数表示间隔多少次迭代输出一次信息 ) 1. 2. 3. 4. 5. 6...
eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) ...
eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) ...
我正在使用LGBMClassifier进行网格搜索:fit_params={"early_stopping_rounds":30, "eval_metric" : 'auc', "eval_set" : [(X_test_,y_test_)], 'eval_names': ['valid'], 'verbo
但通常情况下，XGBoost 都比另外两个算法慢。 最近，我参加了 kaggle 竞赛 WID
def opt_lgb_0(request): optimizer = BayesianOptPro(target_metric=request.param, iterations=2, random_state=32) optimizer.forge_experiment( model_initializer=LGBMClassifier, model_init_params=dict( boosting_type=Categorical(["gbdt", "dart"]), num_leaves=Integer(2, 8), n_estimators=10, max_...
elif eval_metric in ('error', 'binary_error'): eval_metric = "multi_error" elif eval_metric in ('logloss', 'multi_logloss'): eval_metric = 'binary_logloss' elif eval_metric in ('error', 'multi_error'): eval_metric = 'binary_error' if eval_set is not None: if isinstance(eva...