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

ExtraTreeClassifier - 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.tree._classes.ExtraTreeClassifier'>={'zipmap': False}``.

::

        ExtraTreeClassifier(random_state=0)

+-----------------------+----------+
| index                 | 0        |
+=======================+==========+
| skl_nop               | 1        |
+-----------------------+----------+
| skl_nnodes            | 81       |
+-----------------------+----------+
| skl_ntrees            | 1        |
+-----------------------+----------+
| skl_max_depth         | 14       |
+-----------------------+----------+
| onx_size              | 4468     |
+-----------------------+----------+
| 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        | 4468     |
+-----------------------+----------+
| onx_nnodes_optim      | 1        |
+-----------------------+----------+
| onx_ninits_optim      | 0        |
+-----------------------+----------+
| fit_classes_.shape    | 3        |
+-----------------------+----------+
| fit_n_classes_        | 3        |
+-----------------------+----------+
| fit_tree_.node_count  | 81       |
+-----------------------+----------+
| fit_tree_.leave_count | 41       |
+-----------------------+----------+
| fit_tree_.max_depth   | 14       |
+-----------------------+----------+


.. 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)\nclass_ids=[0 1 2 0 1 2 0 1 2 0 ...\nclass_nodeids=[ 4  4  4  5  5  ...\nclass_treeids=[0 0 0 0 0 0 0 0 ...\nclass_weights=[1. 0. 0. 0. 1. 0...\nclasslabels_int64s=[0 1 2]\nnodes_falsenodeids=[ 8  7  6  5...\nnodes_featureids=[0 1 3 2 0 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0  1  2  3  4  ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1  2  3  4 ...\nnodes_values=[5.3849406  3.1319...\npost_transform=b'NONE'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> probabilities;
    }