ai.onnx.ml - TreeEnsembleRegressor#

TreeEnsembleRegressor - 1#

Version

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Summary

Attributes

  • aggregate_function - STRING : Defines how to aggregate leaf values within a target. <br>One of ‘AVERAGE,’ ‘SUM,’ ‘MIN,’ ‘MAX.’

  • base_values - FLOATS : Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)

  • n_targets - INT : The total number of targets.

  • nodes_falsenodeids - INTS : Child node if expression is false

  • nodes_featureids - INTS : Feature id for each node.

  • nodes_hitrates - FLOATS : Popularity of each node, used for performance and may be omitted.

  • nodes_missing_value_tracks_true - INTS : For each node, define what to do in the presence of a NaN: use the ‘true’ (if the attribute value is 1) or ‘false’ (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.

  • nodes_modes - STRINGS : The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of ‘BRANCH_LEQ’, ‘BRANCH_LT’, ‘BRANCH_GTE’, ‘BRANCH_GT’, ‘BRANCH_EQ’, ‘BRANCH_NEQ’, ‘LEAF’

  • nodes_nodeids - INTS : Node id for each node. Node ids must restart at zero for each tree and increase sequentially.

  • nodes_treeids - INTS : Tree id for each node.

  • nodes_truenodeids - INTS : Child node if expression is true

  • nodes_values - FLOATS : Thresholds to do the splitting on for each node.

  • post_transform - STRING : Indicates the transform to apply to the score. <br>One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’

  • target_ids - INTS : The index of the target that each weight is for

  • target_nodeids - INTS : The node id of each weight

  • target_treeids - INTS : The id of the tree that each node is in.

  • target_weights - FLOATS : The weight for each target

Inputs

  • X (heterogeneous) - T:

Outputs

  • Y (heterogeneous) - tensor(float):

Type Constraints

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