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 > 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 <= 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.
Learning rate for shrinking the contribution of each estimator.
Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]
Loss function used for minimization during gradient boosting.
Loss function used for minimization during gradient boosting.
Number of iterations of boosting.
Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model.
Parameters for the tree algorithm.
Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored.
validationTol is a condition which decides iteration termination when runWithValidation is used.
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 > 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 <= 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.
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 > 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 <= 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.