.. _l-RandomForestRegressor-~m-reg-64-default--o17:

RandomForestRegressor - ~m-reg-64 - default - 
==============================================

Fitted on a problem type *~m-reg-64*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict` matches output .


::

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

+---------------------------------------+----------+
| index                                 | 0        |
+=======================================+==========+
| skl_nop                               | 11       |
+---------------------------------------+----------+
| skl_nnodes                            | 1388     |
+---------------------------------------+----------+
| skl_ntrees                            | 10       |
+---------------------------------------+----------+
| skl_max_depth                         | 14       |
+---------------------------------------+----------+
| onx_size                              | 72030    |
+---------------------------------------+----------+
| 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                        | 72030    |
+---------------------------------------+----------+
| onx_nnodes_optim                      | 2        |
+---------------------------------------+----------+
| onx_ninits_optim                      | 0        |
+---------------------------------------+----------+
| fit_estimators_.size                  | 10       |
+---------------------------------------+----------+
| fit_estimators_.sum|tree_.leave_count | 699      |
+---------------------------------------+----------+
| fit_estimators_.max|tree_.max_depth   | 14       |
+---------------------------------------+----------+
| fit_estimators_.sum|tree_.node_count  | 1388     |
+---------------------------------------+----------+


.. gdot::

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

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

      variable [shape=box color=green label="variable\ndouble((0, 1))" fontsize=10];


      tree_ensemble_cast [shape=box label="tree_ensemble_cast" fontsize=10];
      TreeEnsembleRegressor [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor)\nn_targets=2\nnodes_falsenodeids=[46 33 30 .....\nnodes_featureids=[2 2 2 ... 0 0...\nnodes_hitrates_as_tensor=[1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 ......\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[  0   1   2 ... ...\nnodes_treeids=[0 0 0 ... 9 9 9]\nnodes_truenodeids=[1 2 3 ... 0 ...\nnodes_values_as_tensor=[2.7556253...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 ... 1 0 1]\ntarget_nodeids=[  5   5   7 ......\ntarget_treeids=[0 0 0 ... 9 9 9...\ntarget_weights_as_tensor=[0.013 0.0..." fontsize=10];
      X -> TreeEnsembleRegressor;
      TreeEnsembleRegressor -> 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 -> variable;
    }