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

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

Fitted on a problem type *m-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                                 | 2876     |
+--------------------------------------------+----------+
| skl_ntrees                                 | 200      |
+--------------------------------------------+----------+
| skl_max_depth                              | 3        |
+--------------------------------------------+----------+
| onx_size                                   | 310105   |
+--------------------------------------------+----------+
| 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                             | 310105   |
+--------------------------------------------+----------+
| onx_nnodes_optim                           | 1        |
+--------------------------------------------+----------+
| onx_ninits_optim                           | 0        |
+--------------------------------------------+----------+
| fit_classes_.shape                         | 3        |
+--------------------------------------------+----------+
| fit_estimators_.shape                      | 3        |
+--------------------------------------------+----------+
| fit_train_score_.shape                     | 200      |
+--------------------------------------------+----------+
| fit_n_classes_                             | 3        |
+--------------------------------------------+----------+
| fit_estimators_.size                       | 200      |
+--------------------------------------------+----------+
| fit_estimators_.sum|.sum|tree_.leave_count | 4382     |
+--------------------------------------------+----------+
| fit_estimators_.sum|.sum|tree_.node_count  | 8164     |
+--------------------------------------------+----------+
| 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, 3))" fontsize=10];


      TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nbase_values=[-1.1631508 -1.0549...\nclass_ids=[0 0 1 ... 2 2 2]\nclass_nodeids=[ 1  2  3 ... 11 ...\nclass_treeids=[  0   0   1 ... ...\nclass_weights=[ 0.21333334 -0.0...\nclasslabels_int64s=[0 1 2]\nnodes_falsenodeids=[ 2  0  0 .....\nnodes_featureids=[2 0 0 ... 0 0...\nnodes_hitrates=[1. 1. 1. ... 1....\nnodes_missing_value_tracks_true=[0 0 0 ......\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[ 0  1  2 ... 12 ...\nnodes_treeids=[  0   0   0 ... ...\nnodes_truenodeids=[ 1  0  0 ......\nnodes_values=[2.5489838 0.     ...\npost_transform=b'SOFTMAX'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> probabilities;
    }