:: Experimental :: Configuration options for org.apache.spark.mllib.tree.GradientBoostedTrees.
:: Experimental :: Stores all the configuration options for tree construction
:: Experimental :: Stores all the configuration options for tree construction
:: Experimental :: Enum to select the algorithm for the decision tree
:: Experimental :: Enum to select the algorithm for the decision tree
:: Experimental :: Enum to describe whether a feature is "continuous" or "categorical"
:: Experimental :: Enum to describe whether a feature is "continuous" or "categorical"
:: Experimental :: Enum for selecting the quantile calculation strategy
:: Experimental :: Enum for selecting the quantile calculation strategy
:: Experimental :: Configuration options for org.apache.spark.mllib.tree.GradientBoostedTrees.
Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored.
Loss function used for minimization during gradient boosting.
Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model.
Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]
Useful when runWithValidation is used. If the error rate on the validation input between two iterations is less than the validationTol then stop. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.