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]
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.
org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.
Stores all the configuration options for tree construction
Enum to select the algorithm for the decision tree
Enum to describe whether a feature is "continuous" or "categorical"
Enum for selecting the quantile calculation strategy