How to use lightgbm.cv for regression?

Question

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         ) 

The task is to do regression, but the following code throws an error:

Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead. 

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

Answer 1

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

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

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         ) 

Now its working.