Python：LightGBM交叉验证。如何使用lightgbm.cv进行回归？ [英] Python: LightGBM cross validation. How to use lightgbm.cv for regression?

问题描述

我想使用 lgb.Dataset 对LightGBM模型进行交叉验证，并使用 early_stopping_rounds 。 XGBoost的 xgboost.cv 使用以下方法时不会出现问题。我不想在GridSearchCV中使用Scikit Learn的方法，因为它不支持提早停止或lgb.Dataset。

I want to do a cross validation for LightGBM model with lgb.Dataset and use early_stopping_rounds. The following approach works without a problem with XGBoost's xgboost.cv. I prefer not to use Scikit Learn's approach with GridSearchCV, because it doesn't support early stopping or lgb.Dataset.

import lightgbm as lgb
from sklearn.metrics import mean_absolute_error
dftrainLGB = lgb.Dataset(data = dftrain, label = ytrain, feature_name = list(dftrain))

params = {'objective': 'regression'}

cv_results = lgb.cv(
        params,
        dftrainLGB,
        num_boost_round=100,
        nfold=3,
        metrics='mae',
        early_stopping_rounds=10
        )

任务是进行回归，但是以下代码引发错误：
支持的目标类型为：（'binary'，'multiclass'）。而是连续。

The task is to do regression, but the following code throws an error: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.

LightGBM支持回归，还是我提供了错误的参数？

Does LightGBM support regression, or did I supply wrong parameters?

推荐答案

默认情况下，lightgbm.cv中的stratify参数为 True 。
根据文档：

By default, the stratify parameter in the lightgbm.cv is True. According to the documentation:

分层（布尔型，可选（默认= True））–是否执行
分层抽样。

stratified (bool, optional (default=True)) – Whether to perform stratified sampling.

但是分层仅适用于分类问题。因此，要使用回归，您需要将其设为False。

But stratify works only with classification problems. So to work with regression, you need to make it False.

cv_results = lgb.cv(
        params,
        dftrainLGB,
        num_boost_round=100,
        nfold=3,
        metrics='mae',
        early_stopping_rounds=10,

        # This is what I added
        stratified=False
        )

现在可以正常工作了。

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