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

RandomForestClassifier - 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._forest.RandomForestClassifier'>={'zipmap': False}``.

::

        RandomForestClassifier(n_estimators=10, n_jobs=8, random_state=0)

+---------------------------------------+----------+
| index                                 | 0        |
+=======================================+==========+
| skl_nop                               | 11       |
+---------------------------------------+----------+
| skl_nnodes                            | 224      |
+---------------------------------------+----------+
| skl_ntrees                            | 10       |
+---------------------------------------+----------+
| skl_max_depth                         | 8        |
+---------------------------------------+----------+
| onx_size                              | 11385    |
+---------------------------------------+----------+
| 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                        | 11385    |
+---------------------------------------+----------+
| onx_nnodes_optim                      | 1        |
+---------------------------------------+----------+
| onx_ninits_optim                      | 0        |
+---------------------------------------+----------+
| fit_classes_.shape                    | 3        |
+---------------------------------------+----------+
| fit_n_classes_                        | 3        |
+---------------------------------------+----------+
| fit_estimators_.size                  | 10       |
+---------------------------------------+----------+
| fit_estimators_.n_classes_            | 3        |
+---------------------------------------+----------+
| fit_estimators_.classes_.shape        | 3        |
+---------------------------------------+----------+
| fit_estimators_.sum|tree_.leave_count | 117      |
+---------------------------------------+----------+
| fit_estimators_.max|tree_.max_depth   | 8        |
+---------------------------------------+----------+
| fit_estimators_.sum|tree_.node_count  | 224      |
+---------------------------------------+----------+


.. 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=[ 2  2  2  3  3  ...\nclass_treeids=[0 0 0 0 0 0 0 0 ...\nclass_weights=[0.1 0.  0.  0.  ...\nclasslabels_int64s=[0 1 2]\nnodes_falsenodeids=[ 4  3  0  0...\nnodes_featureids=[3 2 0 0 3 2 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  0  0 ...\nnodes_values=[0.6453129  2.8345...\npost_transform=b'NONE'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> probabilities;
    }