Spark Gradient-Boosted Trees Learner (MLlib)

This node applies the Apache Spark Gradient-Boosted Trees (GBTs) algorithm.

Note: GBTs do not yet support multiclass classification. The class column needs to contain 0 and 1 as class labels. For multiclass problems, please use decision trees or Random Forests.

Please note that all data must be numeric, including the label column which needs to contain 0 and 1. Use the Spark Category To Number nodes to convert nominal values to numeric columns. The mapping dictionary of the converter node is the input of the second in port of this node.

Use the Spark Predictor node to apply the learned model to unseen data.



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)


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

Input Ports

Input Spark DataFrame/RDD
PMML with the nominal values mapping dictionary

Output Ports

Spark MLlib Gradient-Boosted Trees Model


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