.. _l-GradientBoostingClassifier-b-cl-default-zipmap:False-o17:

GradientBoostingClassifier - b-cl - default - {'zipmap': False}
===============================================================

Fitted on a problem type *b-cl*
(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._gb.GradientBoostingClassifier'>={'zipmap': False}``.

::

        GradientBoostingClassifier(n_estimators=200, random_state=0)

+--------------------------------------------+----------+
| index                                      | 0        |
+============================================+==========+
| skl_nop                                    | 201      |
+--------------------------------------------+----------+
| skl_nnodes                                 | 554      |
+--------------------------------------------+----------+
| skl_ntrees                                 | 200      |
+--------------------------------------------+----------+
| skl_max_depth                              | 3        |
+--------------------------------------------+----------+
| onx_size                                   | 21641    |
+--------------------------------------------+----------+
| 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                             | 21641    |
+--------------------------------------------+----------+
| onx_nnodes_optim                           | 1        |
+--------------------------------------------+----------+
| onx_ninits_optim                           | 0        |
+--------------------------------------------+----------+
| fit_classes_.shape                         | 2        |
+--------------------------------------------+----------+
| fit_estimators_.shape                      | 1        |
+--------------------------------------------+----------+
| fit_train_score_.shape                     | 200      |
+--------------------------------------------+----------+
| fit_n_classes_                             | 2        |
+--------------------------------------------+----------+
| fit_estimators_.size                       | 200      |
+--------------------------------------------+----------+
| fit_estimators_.sum|.sum|tree_.leave_count | 377      |
+--------------------------------------------+----------+
| fit_estimators_.sum|.sum|tree_.node_count  | 554      |
+--------------------------------------------+----------+
| fit_estimators_.max|.max|tree_.max_depth   | 3        |
+--------------------------------------------+----------+


.. gdot::

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

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

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


      TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nbase_values=[0.78845733]\nclass_ids=[0 0 0 0 0 0 0 0 0 0 ...\nclass_nodeids=[1 2 1 2 1 2 1 3 ...\nclass_treeids=[  0   0   1   1 ...\nclass_weights=[-3.19999993e-01 ...\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0 2 0 0...\nnodes_featureids=[2 0 0 2 0 0 2...\nnodes_hitrates=[1. 1. 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 0 1 ...\nnodes_treeids=[  0   0   0   1 ...\nnodes_truenodeids=[1 0 0 1 0 0 ...\nnodes_values=[2.5489838 0.     ...\npost_transform=b'LOGISTIC'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> probabilities;
    }