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...
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...
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, _...
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, _...
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
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) ...
eval_set=(x12_valid, y12_valid), eval_metric="logloss", callbacks=[lgbm.log_evaluation(period=100), lgbm.early_stopping(stopping_rounds=100)]) The result will be this The binary_logloss starts with 0.693169 and end with 0.697759 and no stopping happened ...
(self.stopping_rounds, first_metric_only=True)], 'eval_metric' :self.eval_metric, } else: X_train, y_train = X, y param_eval = {} self.estimator.fit(X_train, y_train, **param_eval) return self def predict(self, X): return self.estimator.predict(X) def predict_p...