Class BoostingStrategy

Object
org.apache.spark.mllib.tree.configuration.BoostingStrategy
All Implemented Interfaces:
Serializable, scala.Equals, scala.Product, scala.Serializable

public class BoostingStrategy extends Object implements scala.Serializable, scala.Product
Configuration options for GradientBoostedTrees.

param: treeStrategy Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored. param: loss Loss function used for minimization during gradient boosting. param: numIterations Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model. param: learningRate Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1] param: validationTol validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

See Also:
  • Constructor Details

    • BoostingStrategy

      public BoostingStrategy(Strategy treeStrategy, Loss loss, int numIterations, double learningRate, double validationTol)
  • Method Details

    • defaultParams

      public static BoostingStrategy defaultParams(String algo)
      Returns default configuration for the boosting algorithm
      Parameters:
      algo - Learning goal. Supported: "Classification" or "Regression"
      Returns:
      Configuration for boosting algorithm
    • defaultParams

      public static BoostingStrategy defaultParams(scala.Enumeration.Value algo)
      Returns default configuration for the boosting algorithm
      Parameters:
      algo - Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression
      Returns:
      Configuration for boosting algorithm
    • treeStrategy

      public Strategy treeStrategy()
    • loss

      public Loss loss()
    • numIterations

      public int numIterations()
    • learningRate

      public double learningRate()
    • validationTol

      public double validationTol()
    • getLearningRate

      public double getLearningRate()
    • getLoss

      public Loss getLoss()
    • getNumIterations

      public int getNumIterations()
    • getTreeStrategy

      public Strategy getTreeStrategy()
    • getValidationTol

      public double getValidationTol()
    • setLearningRate

      public void setLearningRate(double x$1)
    • setLoss

      public void setLoss(Loss x$1)
    • setNumIterations

      public void setNumIterations(int x$1)
    • setTreeStrategy

      public void setTreeStrategy(Strategy x$1)
    • setValidationTol

      public void setValidationTol(double x$1)