GBT Regression

Gradient-Boosted Trees (GBTs) is a learning algorithm for regression. It supports both continuous and categorical features.

This operation is ported from Spark ML.

For a comprehensive introduction, see Spark documentation.

Since: Seahorse 1.0.0

Input

This operation does not take any input.

Port	Type Qualifier	Description
`0`	`Estimator`	An `Estimator` that can be used in a Fit operation.

Name	Type	Description
`regression impurity`	`SingleChoice`	The criterion used for information gain calculation. Possible values: `["variance"]`
`loss function`	`SingleChoice`	The loss function which GBT tries to minimize. Possible values: `["squared", "absolute"]`
`max bins`	`Numeric`	The maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be >= 2 and >= number of categories in any categorical feature.
`max depth`	`Numeric`	The maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
`max iterations`	`Numeric`	The maximum number of iterations.
`min information gain`	`Numeric`	The minimum information gain for a split to be considered at a tree node.
`min instances per node`	`Numeric`	The minimum number of instances each child must have after split. If a split causes the left or right child to have fewer instances than the parameter's value, the split will be discarded as invalid.
`seed`	`Numeric`	The random seed.
`step size`	`Numeric`	The step size to be used for each iteration of optimization.
`subsampling rate`	`Numeric`	The fraction of the training data used for learning each decision tree.
`label column`	`SingleColumnSelector`	The label column for model fitting.
`features column`	`SingleColumnSelector`	The features column for model fitting.
`prediction column`	`String`	The prediction column created during model scoring.