Tree Featurization Prediction

Prediction Details

This estimator produces several output columns from a tree ensemble model. Assume that the model contains only one decision tree:

               Node 0
               /    \
             /        \
           /            \
         /                \
       Node 1            Node 2
       /    \            /    \
     /        \        /        \
   /            \     Leaf -3  Node 3
  Leaf -1      Leaf -2         /    \
                             /        \
                            Leaf -4  Leaf -5

Assume that the input feature vector falls into Leaf -1. The output Trees may be a 1-element vector where the only value is the decision value carried by Leaf -1. The output Leaves is a 0-1 vector. If the reached leaf is the $i$-th (indexed by $-(i+1)$ so the first leaf is Leaf -1) leaf in the tree, the $i$-th value in Leaves would be 1 and all other values would be 0. The output Paths is a 0-1 representation of the nodes passed through before reaching the leaf. The $i$-th element in Paths indicates if the $i$-th node (indexed by $i$) is touched. For example, reaching Leaf -1 lead to $[1, 1, 0, 0]$ as the Paths. If there are multiple trees, this estimator just concatenates Trees's, Leaves's, Paths's from all trees (first tree's information comes first in the concatenated vectors).

Check the See Also section for links to usage examples.