.. _l-XGBClassifier-~b-cl-64-default--o17:

XGBClassifier - ~b-cl-64 - default - 
=====================================

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


::

        XGBClassifier(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             | 5902            |
+----------------------+-----------------+
| onx_nnodes           | 4               |
+----------------------+-----------------+
| 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          | 2               |
+----------------------+-----------------+
| onx_op_ZipMap        | 1               |
+----------------------+-----------------+
| onx_size_optim       | 5902            |
+----------------------+-----------------+
| onx_nnodes_optim     | 4               |
+----------------------+-----------------+
| onx_ninits_optim     | 0               |
+----------------------+-----------------+
| fit_classes_.shape   | 2               |
+----------------------+-----------------+
| fit_n_classes_       | 2               |
+----------------------+-----------------+
| fit_objective        | binary:logistic |
+----------------------+-----------------+
| fit_estimators_.size | 100             |
+----------------------+-----------------+
| fit_node_count       | 128             |
+----------------------+-----------------+
| fit_ntrees           | 100             |
+----------------------+-----------------+
| fit_leave_count      | 114             |
+----------------------+-----------------+
| fit_mode_count       | 2               |
+----------------------+-----------------+


.. gdot::

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

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

      output_label [shape=box color=green label="output_label\nint64((0,))" fontsize=10];
      output_probability [shape=box color=green label="output_probability\n[{int64, {'kind': 'tensor', 'elem': 'double', 'shape': }}]" fontsize=10];


      label [shape=box label="label" fontsize=10];
      tree_ensemble_cast [shape=box label="tree_ensemble_cast" fontsize=10];
      TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nclass_ids=[0 0 0 0 0 0 0 0 0 0 ...\nclass_nodeids=[1 2 1 2 1 2 1 2 ...\nclass_treeids=[ 0  0  1  1  2  ...\nclass_weights_as_tensor=[-5.384615...\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0 2 0 0...\nnodes_featureids=[2 0 0 2 0 0 2...\nnodes_missing_value_tracks_true=[1 0 0 1 0...\nnodes_modes=[b'BRANCH_LT' b'LEA...\nnodes_nodeids=[0 1 2 0 1 2 0 1 ...\nnodes_treeids=[ 0  0  0  1  1  ...\nnodes_truenodeids=[1 0 0 1 0 0 ...\nnodes_values_as_tensor=[2.5489840...\npost_transform=b'LOGISTIC'" fontsize=10];
      X -> TreeEnsembleClassifier;
      TreeEnsembleClassifier -> label;
      TreeEnsembleClassifier -> tree_ensemble_cast;

      Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=7" fontsize=10];
      label -> Cast;
      Cast -> output_label;

      probabilities [shape=box label="probabilities" fontsize=10];
      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 -> probabilities;

      ZipMap [shape=box style="filled,rounded" color=orange label="ZipMap\n(ZipMap)\nclasslabels_int64s=[0 1]" fontsize=10];
      probabilities -> ZipMap;
      ZipMap -> output_probability;
    }