.. _l-HistGradientBoostingClassifier-~b-cl-64-default-zipmap:False-o17:

HistGradientBoostingClassifier - ~b-cl-64 - default - {'zipmap': False}
=======================================================================

Fitted on a problem type *~b-cl-64*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict_proba` matches output .
Model was converted with additional parameter: ``<class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>={'zipmap': False}``.

::

        HistGradientBoostingClassifier(random_state=0)

+---------------------------------------+----------+
| index                                 | 0        |
+=======================================+==========+
| skl_nop                               | 1        |
+---------------------------------------+----------+
| onx_size                              | 15210    |
+---------------------------------------+----------+
| onx_nnodes                            | 2        |
+---------------------------------------+----------+
| 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_                                  | 13       |
+---------------------------------------+----------+
| onx_ai.onnx.ml                        | 3        |
+---------------------------------------+----------+
| onx_op_Cast                           | 1        |
+---------------------------------------+----------+
| onx_size_optim                        | 15210    |
+---------------------------------------+----------+
| onx_nnodes_optim                      | 2        |
+---------------------------------------+----------+
| onx_ninits_optim                      | 0        |
+---------------------------------------+----------+
| fit_classes_.shape                    | 2        |
+---------------------------------------+----------+
| fit_train_score_.shape                | 0        |
+---------------------------------------+----------+
| fit_validation_score_.shape           | 0        |
+---------------------------------------+----------+
| fit__predictors.size                  | 100      |
+---------------------------------------+----------+
| fit__predictors.sum|tree_.leave_count | 208      |
+---------------------------------------+----------+
| fit__predictors.max|tree_.max_depth   | 2        |
+---------------------------------------+----------+
| fit__predictors.sum|tree_.node_count  | 316      |
+---------------------------------------+----------+


.. gdot::

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

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

      label [shape=box color=green label="label\nint64((0,))" fontsize=10];
      probabilities [shape=box color=green label="probabilities\ndouble((0, 2))" fontsize=10];


      tree_ensemble_cast [shape=box label="tree_ensemble_cast" fontsize=10];
      TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nbase_values_as_tensor=[0.7884573...\nclass_ids=[0 0 0 0 0 0 0 0 0 0 ...\nclass_nodeids=[1 2 1 3 4 1 3 4 ...\nclass_treeids=[ 0  0  1  1  1  ...\nclass_weights_as_tensor=[-0.32    ...\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0 2 0 4...\nnodes_featureids=[2 0 0 2 0 0 0...\nnodes_hitrates_as_tensor=[1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2 0 1 2 3 4 ...\nnodes_treeids=[ 0  0  0  1  1  ...\nnodes_truenodeids=[1 0 0 1 0 3 ...\nnodes_values_as_tensor=[2.5489840...\npost_transform=b'LOGISTIC'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> tree_ensemble_cast;

      tree_ensemble_cast [shape=box style="filled,rounded" color=orange label="Cast\n(tree_ensemble_cast)\nto=11" fontsize=10];
      tree_ensemble_cast -> tree_ensemble_cast;
      tree_ensemble_cast -> probabilities;
    }