.. _l-onnx-opai-onnx-ml-treeensembleregressor-d1-3:

TreeEnsembleRegressor - 1 vs 3
==============================

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

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    --- a/TreeEnsembleRegressor1
    +++ b/TreeEnsembleRegressor3
    @@ -1 +1 @@
      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 classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
    ?                   ^^^  ------                    ^^^^^^^^^
    +   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.
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