.. _l-XGBRegressor-b-reg-default--o17:

XGBRegressor - b-reg - default - 
=================================

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


::

        XGBRegressor(base_score=None, booster=None, callbacks=None,
                 colsample_bylevel=None, colsample_bynode=None,
                 colsample_bytree=None, early_stopping_rounds=None,
                 enable_categorical=False, eval_metric=None, feature_types=None,
                 gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
                 interaction_constraints=None, learning_rate=None, max_bin=None,
                 max_cat_threshold=None, max_cat_to_onehot=None,
                 max_delta_step=None, max_depth=None, max_leaves=None,
                 min_child_weight=None, missing=nan, monotone_constraints=None,
                 n_estimators=100, n_jobs=None, num_parallel_tree=None,
                 predictor=None, random_state=None, ...)

+----------------------+------------------+
| index                | 0                |
+======================+==================+
| skl_nop              | 1                |
+----------------------+------------------+
| onx_size             | 81519            |
+----------------------+------------------+
| 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       | 1                |
+----------------------+------------------+
| onx_                 | 17               |
+----------------------+------------------+
| onx_size_optim       | 81519            |
+----------------------+------------------+
| onx_nnodes_optim     | 1                |
+----------------------+------------------+
| onx_ninits_optim     | 0                |
+----------------------+------------------+
| fit_objective        | reg:squarederror |
+----------------------+------------------+
| fit_estimators_.size | 100              |
+----------------------+------------------+
| fit_node_count       | 2602             |
+----------------------+------------------+
| fit_ntrees           | 100              |
+----------------------+------------------+
| fit_leave_count      | 1351             |
+----------------------+------------------+
| fit_mode_count       | 2                |
+----------------------+------------------+


.. gdot::

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

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

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


      TreeEnsembleRegressor [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor)\nbase_values=[0.5]\nn_targets=1\nnodes_falsenodeids=[16 13  4 .....\nnodes_featureids=[2 2 1 ... 0 0...\nnodes_missing_value_tracks_true=[1 1 1 ......\nnodes_modes=[b'BRANCH_LT' b'BRA...\nnodes_nodeids=[0 1 2 ... 0 0 0]\nnodes_treeids=[ 0  0  0 ... 97 ...\nnodes_truenodeids=[1 2 3 ... 0 ...\nnodes_values=[2.548984  1.73063...\npost_transform=b'NONE'\ntarget_ids=[0 0 0 ... 0 0 0]\ntarget_nodeids=[3 6 7 ... 0 0 0...\ntarget_treeids=[ 0  0  0 ... 97...\ntarget_weights=[-2.1000002e-02 ..." fontsize=10];
      X -> TreeEnsembleRegressor;
      TreeEnsembleRegressor -> variable;
    }