ai.onnx.ml - TreeEnsembleClassifier#

TreeEnsembleClassifier - 1#

Version

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

Summary

Attributes

  • 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)

  • class_ids - INTS : The index of the class list that each weight is for.

  • class_nodeids - INTS : node id that this weight is for.

  • class_treeids - INTS : The id of the tree that this node is in.

  • class_weights - FLOATS : The weight for the class in class_id.

  • classlabels_int64s - INTS : Class labels if using integer labels.<br>One and only one of the ‘classlabels_*’ attributes must be defined.

  • classlabels_strings - STRINGS : Class labels if using string labels.<br>One and only one of the ‘classlabels_*’ attributes must be defined.

  • 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 missing value: if a value is missing (NaN), use the ‘true’ or ‘false’ 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. Ids may restart at zero for each tree, but it not required to.

  • 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.’

Inputs

  • X (heterogeneous) - T1:

Outputs

  • Y (heterogeneous) - T2:

  • Z (heterogeneous) - tensor(float):

Type Constraints

  • T1 in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input type must be a tensor of a numeric type.

  • T2 in ( tensor(int64), tensor(string) ): The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.