Spark Gradient-Boosted Trees Learner

Options

Number of iterations

Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model.

Max number of bins

Maximum number of bins used for splitting features (suggested value: 100).

Max depth

Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (suggested value: 4)

Loss function

Loss function used for minimization during gradient boosting. Available functions: Log Loss, Squared Error and Absolute Error. For details about the loss functions see the Losses documentation.

Is classification

Select this option for binary classification.

Learning rate

Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]

Class column

The classification column. Must be numeric.

Feature Columns

The feature columns to learn the model from. Supports only numeric columns.

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