Overview of the Boosted Tree Platform
The Boosted Tree platform produces an additive decision tree model that is based on many smaller decision trees that are constructed in layers. The tree in each layer consists of a small number of splits, typically five or fewer. Each layer is fit using the recursive fitting methodology described in the Partition Models chapter. The only difference is that fitting stops at a specified number of splits. For a given tree, the predicted value for an observation in a leaf is the mean of all observations in that leaf.
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Repeat step 2 to step 4 until the specified number of layers is reached, or, if validation is used, until fitting an additional layer no longer improves the validation statistic.

Help created on 7/12/2018