result = super(LGBMClassifier, self).predict(X, raw_score, num_iteration, pred_leaf, pred_contrib, **kwargs). if self._n_classes > 2 or pred_leaf or pred_contrib: return result else: return np.vstack((1. - resul
feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self._classes = self._le.classes_ self._n_...
def predict(self, X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs): """Docstring is inherited from the LGBMModel.""" result = self.predict_proba(X, raw_score, num_iteration, pred_leaf, pred_contrib, **kwargs) if raw_score or pred_leaf or p...
pred_leaf=False, pred_contrib=False, **kwargs): """Docstring is inherited from the LGBMModel.""" result = self.predict_proba(X, raw_score, num_iteration, pred_leaf, pred_contrib, **kwargs) if raw_score or pred_leaf or pred_contrib: return result else: class_index = np.argmax(re...
LGBMClassifier函数的调参技巧 1、lightGBM适合较大数据集的样本 而对于较小的数据集(<10000条记录),lightGBM可能不是最佳选择。所以,如果进行调优lightgbm参数,这可能没有帮助。 2、建议使用更小的learning_rate和更大的num_iteration 此外,如果您想要更高的num_iteration,那么您应该使用early_stopping_rounds,以便在无...
LGBMClassifier函数的调参技巧 1、lightGBM适合较大数据集的样本 而对于较小的数据集(<10000条记录),lightGBM可能不是最佳选择。所以,如果进行调优lightgbm参数,这可能没有帮助。 2、建议使用更小的learning_rate和更大的num_iteration 此外,如果您想要更高的num_iteration,那么您应该使用early_stopping_rounds,以便在无...