Class: GradientBoostedTrees

eclairjs/mllib/tree.GradientBoostedTrees

new GradientBoostedTrees(boostingStrategy)

Parameters:
Name Type Description
boostingStrategy module:eclairjs/mllib/tree/configuration.BoostingStrategy
Source:

Methods

(static) train(input, boostingStrategy) → {GradientBoostedTreesModel}

Method to train a gradient boosting model.
Parameters:
Name Type Description
input module:eclairjs/rdd.RDD Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
boostingStrategy module:eclairjs/mllib/tree/configuration.BoostingStrategy Configuration options for the boosting algorithm.
Source:
Returns:
a gradient boosted trees model that can be used for prediction
Type
GradientBoostedTreesModel

run(input) → {GradientBoostedTreesModel}

Method to train a gradient boosting model
Parameters:
Name Type Description
input module:eclairjs/rdd.RDD Training dataset: RDD of LabeledPoint.
Source:
Returns:
a gradient boosted trees model that can be used for prediction
Type
GradientBoostedTreesModel

runWithValidation(input, validationInput) → {GradientBoostedTreesModel}

Method to validate a gradient boosting model
Parameters:
Name Type Description
input module:eclairjs/rdd.RDD Training dataset: RDD of LabeledPoint.
validationInput module:eclairjs/rdd.RDD Validation dataset. This dataset should be different from the training dataset, but it should follow the same distribution. E.g., these two datasets could be created from an original dataset by using [[org.apache.spark.rdd.RDD.randomSplit()]]
Source:
Returns:
a gradient boosted trees model that can be used for prediction
Type
GradientBoostedTreesModel