Table 2 Hyperparameters

 Learning rate

Shrinkage rate (how much will the weights be adjusted every iteration).

 Number of leaves

Maximum number of leaves in one tree.

 Lambda L1

L1 regularization.

 Lambda L2

L2 regularization.

 Feature fraction

Randomly select part of the predictors on each iteration.

 Early stopping

The cross-validation score needs to improve at least every n round to continue with the next boosting iteration.

 Maximum depth

Maximum tree depth (note that it is less relevant here since the tree grows leaf-wise).

 Minimum data

Minimal number of records in one leaf. A higher number prevents overfitting.

 Bagging fraction

Randomly select part of the data without resampling.

 Bagging frequency

Per how many rounds should bagging be applied.

 Unbalanced data

Does data need to be balanced or not.