TreeEnsembleRegressor - version 3#

This page documents version 3 of operator TreeEnsembleRegressor. See TreeEnsembleRegressor for the latest version (since version 5).

  • Domain: ai.onnx.ml

  • Since version: 3

Tree Ensemble regressor. Returns the regressed values for each input in N. All args with nodes are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id. All fields prefixed with target are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated target_weights index. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1. Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF

Inputs

  • X (T): Input of shape [N,F]

Outputs

  • Y (tensor(float)): N classes

Type Constraints

  • T: The input type must be a tensor of a numeric type. Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

Differences with previous version (1)#

SchemaDiff: TreeEnsembleRegressor (domain 'ai.onnx.ml')

  • old version: 1

  • new version: 3

  • breaking: no

Documentation:

  • line similarity: 0.92 (+2/-0 lines)

--- TreeEnsembleRegressor v1
+++ TreeEnsembleRegressor v3
@@ -7,5 +7,7 @@
     All fields prefixed with target_ are tuples of votes at the leaves.<br>
     A leaf may have multiple votes, where each vote is weighted by
     the associated target_weights index.<br>
+    All fields ending with <i>_as_tensor</i> can be used instead of the
+    same parameter without the suffix if the element type is double and not float.
     All trees must have their node ids start at 0 and increment by 1.<br>
     Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF