重现LightGBM自定义损失函数以进行回归 [英] Reproduce LightGBM Custom Loss Function for Regression

问题描述

我想为LightGBM重现自定义丢失功能.这是我尝试过的:

I want to reproduce the custom loss function for LightGBM. This is what I tried:

lgb.train(params=params, train_set=dtrain, num_boost_round=num_round, fobj=default_mse_obj)

将default_mse_obj定义为:

With default_mse_obj being defined as:

residual = y_true - y_pred.get_label()
grad = -2.0*residual
hess = 2.0+(residual*0)
return grad, hess

但是，与定义的自定义损失函数相比，默认回归"目标的评估指标有所不同.我想知道，LightGBM用于回归"目标的默认函数是什么?

However, eval metrics are different for the default "regression" objective, compared to the custom loss function defined. I would like to know, what is the default function used by LightGBM for the "regression" objective?

推荐答案

如您所见这里，这是回归任务的默认损失函数

as you can see here, this is the default loss function for regression task

def default_mse_obj(y_pred, dtrain):

    y_true = dtrain.get_label()

    grad = (y_pred - y_true)
    hess = np.ones(len(grad))

    return grad, hess

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