feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y)
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. - result, result)).transpose() @property def classes_(self): """Get the ...
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. - result, result)).transpose() @property def classes_(self): """Get the ...
return self fit.__doc__ = LGBMModel.fit.__doc__ 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_...
LGBMClassifier函数的调参技巧 1、lightGBM适合较大数据集的样本 2、建议使用更小的learning_rate和更大的num_iteration 3、样本不平衡调参技巧 4、调参时,可将参数字典分为两大类 LGBMClassifier函数简介 1、所有弱学习器的参数 2、具体函数解释 LGBMClassifier函数的简介、具体案例、调参技巧 ...
LGBMClassifier函数的简介、具体案例、调参技巧 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...