Spark Gradient-Boosted Trees Learner (MLlib)

Options

Settings

Target column

The target column. Must be numeric.

Feature Columns

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

Number of models

Number of decision tree models to learn, also known as the number iterations to perform. In other words, the number of weak hypotheses used in the final model.

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.

Max number of bins

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

Is classification

Select this option for binary classification.

Quality measure

Criterion used for information gain calculation. Available methods: "gini" (recommended) or "entropy". For more details on the available methods see the MLlib documentation.

Max tree 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)

Advanced

Learning rate

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

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