.. _op_ai_onnx_ml_TreeEnsemble:

TreeEnsemble
============

- **Domain**: ``ai.onnx.ml``
- **Since version**: 5

Tree Ensemble operator.  Returns the regressed values for each input in a batch.
Inputs have dimensions ``[N, F]`` where ``N`` is the input batch size and ``F`` is the number of input features.
Outputs have dimensions ``[N, num_targets]`` where ``N`` is the batch size and ``num_targets`` is the number of targets, which is a configurable attribute.

The encoding of this attribute is split along interior nodes and the leaves of the trees. Notably, attributes with the prefix ``nodes_*`` are associated with interior nodes, and attributes with the prefix ``leaf_*`` are associated with leaves.
The attributes ``nodes_*`` must all have the same length and encode a sequence of tuples, as defined by taking all the ``nodes_*`` fields at a given position.

All fields prefixed with ``leaf_*`` represent tree leaves, and similarly define tuples of leaves and must have identical length.

This operator can be used to implement both the previous ``TreeEnsembleRegressor`` and ``TreeEnsembleClassifier`` nodes.
The ``TreeEnsembleRegressor`` node maps directly to this node and requires changing how the nodes are represented.
The ``TreeEnsembleClassifier`` node can be implemented by adding a ``ArgMax`` node after this node to determine the top class.
To encode class labels, a ``LabelEncoder`` or ``GatherND`` operator may be used.

**Inputs**

- **X** (*T*): Input of shape [Batch Size, Number of Features]

**Outputs**

- **Y** (*T*): Output of shape [Batch Size, Number of targets]

**Type Constraints**

- **T**: The input type must be a tensor of a numeric type.
  Allowed types: tensor(double), tensor(float), tensor(float16).