.. _l-HistGradientBoostingRegressor-b-reg-default--o17:

HistGradientBoostingRegressor - b-reg - default - 
==================================================

Fitted on a problem type *b-reg*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict` matches output .


::

        HistGradientBoostingRegressor(random_state=0)

+---------------------------------------+----------+
| index                                 | 0        |
+=======================================+==========+
| skl_nop                               | 1        |
+---------------------------------------+----------+
| onx_size                              | 27271    |
+---------------------------------------+----------+
| onx_nnodes                            | 1        |
+---------------------------------------+----------+
| onx_ninits                            | 0        |
+---------------------------------------+----------+
| onx_doc_string                        |          |
+---------------------------------------+----------+
| onx_ir_version                        | 8        |
+---------------------------------------+----------+
| onx_domain                            | ai.onnx  |
+---------------------------------------+----------+
| onx_model_version                     | 0        |
+---------------------------------------+----------+
| onx_producer_name                     | skl2onnx |
+---------------------------------------+----------+
| onx_producer_version                  | 1.13.1   |
+---------------------------------------+----------+
| onx_ai.onnx.ml                        | 3        |
+---------------------------------------+----------+
| onx_                                  | 17       |
+---------------------------------------+----------+
| onx_size_optim                        | 27271    |
+---------------------------------------+----------+
| onx_nnodes_optim                      | 1        |
+---------------------------------------+----------+
| onx_ninits_optim                      | 0        |
+---------------------------------------+----------+
| fit_train_score_.shape                | 0        |
+---------------------------------------+----------+
| fit_validation_score_.shape           | 0        |
+---------------------------------------+----------+
| fit__predictors.size                  | 100      |
+---------------------------------------+----------+
| fit__predictors.sum|tree_.leave_count | 412      |
+---------------------------------------+----------+
| fit__predictors.max|tree_.max_depth   | 4        |
+---------------------------------------+----------+
| fit__predictors.sum|tree_.node_count  | 724      |
+---------------------------------------+----------+


.. gdot::

        digraph{
      size=7;
      ranksep=0.25;
      nodesep=0.05;
      orientation=portrait;

      X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10];

      variable [shape=box color=green label="variable\nfloat((0, 1))" fontsize=10];


      TreeEnsembleRegressor [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor)\nbase_values=[1.7886608]\nn_targets=1\nnodes_falsenodeids=[2 0 4 0 0 2...\nnodes_featureids=[2 0 2 0 0 2 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 1 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2 3 4 0 1 2 ...\nnodes_treeids=[ 0  0  0  0  0  ...\nnodes_truenodeids=[1 0 3 0 0 1 ...\nnodes_values=[2.548984  0.     ...\npost_transform=b'NONE'\ntarget_ids=[0 0 0 0 0 0 0 0 0 0...\ntarget_nodeids=[1 3 4 1 3 4 1 3...\ntarget_treeids=[ 0  0  0  1  1 ...\ntarget_weights=[-1.54494643e-01..." fontsize=10];
      X -> TreeEnsembleRegressor;
      TreeEnsembleRegressor -> variable;
    }