new GradientBoostedTrees(boostingStrategy)
Parameters:
| Name | Type | Description |
|---|---|---|
boostingStrategy |
module:eclairjs/mllib/tree/configuration.BoostingStrategy |
Methods
(static) train(input, boostingStrategy) → {module:eclairjs/mllib/tree/model.GradientBoostedTreesModel}
Method to train a gradient boosting model.
Parameters:
| Name | Type | Description |
|---|---|---|
input |
module:eclairjs.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. |
Returns:
a gradient boosted trees model that can be used for prediction
run(input) → {module:eclairjs/mllib/tree/model.GradientBoostedTreesModel}
Method to train a gradient boosting model
Parameters:
| Name | Type | Description |
|---|---|---|
input |
module:eclairjs.RDD | Training dataset: RDD of LabeledPoint. |
Returns:
a gradient boosted trees model that can be used for prediction
runWithValidation(input, validationInput) → {module:eclairjs/mllib/tree/model.GradientBoostedTreesModel}
Method to validate a gradient boosting model
Parameters:
| Name | Type | Description |
|---|---|---|
input |
module:eclairjs.RDD | Training dataset: RDD of LabeledPoint. |
validationInput |
module:eclairjs.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()]] |
Returns:
a gradient boosted trees model that can be used for prediction