title: How to use lightgbm.cv for regression?
body: <p>I want to do a cross validation for LightGBM model with <em>lgb.Dataset</em> and use <em>early_stopping_rounds</em>. The following approach works without a problem with XGBoost's <em>xgboost.cv</em>. I prefer not to use Scikit Learn's approach with GridSearchCV, because it doesn't support early stopping or lgb.Dataset.</p>
  <pre><code>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
          )
  </code></pre>
  <p>The task is to do regression, but the following code throws an error:</p>
  <pre><code>Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.
  </code></pre>
  <p>Does LightGBM support regression, or did I supply wrong parameters?</p>