(l-onnx-docai-onnx-ml-TreeEnsembleRegressor)=

# ai.onnx.ml - TreeEnsembleRegressor


(l-onnx-opai-onnx-ml-treeensembleregressor-5)=

## TreeEnsembleRegressor - 5 (ai.onnx.ml)

### Version

- **name**: [TreeEnsembleRegressor (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md#ai.onnx.ml.TreeEnsembleRegressor)
- **domain**: `ai.onnx.ml`
- **since_version**: `5`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

This version of the operator has been deprecated
**since version 5 of domain ai.onnx.ml**.

### Summary

This operator is DEPRECATED. Please use TreeEnsemble instead which provides the same
functionality.<br>
Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
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.<br>
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

### Attributes

* **aggregate_function - STRING** (default is `SUM`):

  Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'

* **base_values - FLOATS** :

  Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

* **base_values_as_tensor - TENSOR** :

  Base values for regression, added to final prediction after applying aggregate_function; 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_hitrates_as_tensor - TENSOR** :

  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 default 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.

* **nodes_values_as_tensor - TENSOR** :

  Thresholds to do the splitting on for each node.

* **post_transform - STRING** (default is `NONE`):

  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

* **target_weights_as_tensor - TENSOR** :

  The weight for each target

### Inputs

- **X** (heterogeneous) - **T**:

  Input of shape [N,F]

### Outputs

- **Y** (heterogeneous) - **tensor(float)**:

  N classes

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(int32)`, `tensor(int64)` ):

  The input type must be a tensor of a numeric type.

```{toctree}
text_diff_TreeEnsembleRegressor_3_5
```

(l-onnx-opai-onnx-ml-treeensembleregressor-3)=

## TreeEnsembleRegressor - 3 (ai.onnx.ml)

### Version

- **name**: [TreeEnsembleRegressor (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md#ai.onnx.ml.TreeEnsembleRegressor)
- **domain**: `ai.onnx.ml`
- **since_version**: `3`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 3 of domain ai.onnx.ml**.

### Summary

Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
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.<br>
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

### Attributes

* **aggregate_function - STRING** (default is `SUM`):

  Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'

* **base_values - FLOATS** :

  Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

* **base_values_as_tensor - TENSOR** :

  Base values for regression, added to final prediction after applying aggregate_function; 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_hitrates_as_tensor - TENSOR** :

  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 default 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.

* **nodes_values_as_tensor - TENSOR** :

  Thresholds to do the splitting on for each node.

* **post_transform - STRING** (default is `NONE`):

  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

* **target_weights_as_tensor - TENSOR** :

  The weight for each target

### Inputs

- **X** (heterogeneous) - **T**:

  Input of shape [N,F]

### Outputs

- **Y** (heterogeneous) - **tensor(float)**:

  N classes

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(int32)`, `tensor(int64)` ):

  The input type must be a tensor of a numeric type.

```{toctree}
text_diff_TreeEnsembleRegressor_1_5
text_diff_TreeEnsembleRegressor_1_3
```

(l-onnx-opai-onnx-ml-treeensembleregressor-1)=

## TreeEnsembleRegressor - 1 (ai.onnx.ml)

### Version

- **name**: [TreeEnsembleRegressor (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md#ai.onnx.ml.TreeEnsembleRegressor)
- **domain**: `ai.onnx.ml`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

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

### Summary

Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
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.<br>
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 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

### Attributes

* **aggregate_function - STRING** (default is `SUM`):

  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 default 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** (default is `NONE`):

  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**:

  Input of shape [N,F]

### Outputs

- **Y** (heterogeneous) - **tensor(float)**:

  N classes

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(int32)`, `tensor(int64)` ):

  The input type must be a tensor of a numeric type.