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

TweedieRegressor - 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 .


::

        TweedieRegressor()

+----------------------+----------+
| index                | 0        |
+======================+==========+
| skl_nop              | 1        |
+----------------------+----------+
| skl_ncoef            | 4        |
+----------------------+----------+
| skl_nlin             | 1        |
+----------------------+----------+
| onx_size             | 362      |
+----------------------+----------+
| onx_nnodes           | 3        |
+----------------------+----------+
| onx_ninits           | 3        |
+----------------------+----------+
| 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_                 | 16       |
+----------------------+----------+
| onx_op_Reshape       | 1        |
+----------------------+----------+
| onx_size_optim       | 362      |
+----------------------+----------+
| onx_nnodes_optim     | 3        |
+----------------------+----------+
| onx_ninits_optim     | 3        |
+----------------------+----------+
| fit_intercept_.shape | 1        |
+----------------------+----------+
| fit_coef_.shape      | 4        |
+----------------------+----------+


.. 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];

      Ma_MatMulcst [shape=box label="Ma_MatMulcst\nfloat32((4,))\n[ 0.08625456 -0.04117553  0.39916316  0.19531801]" fontsize=10];
      Ad_Addcst [shape=box label="Ad_Addcst\nfloat32((1,))\n[-0.3263043]" fontsize=10];
      Re_Reshapecst [shape=box label="Re_Reshapecst\nint64((2,))\n[-1  1]" fontsize=10];

      Ma_Y0 [shape=box label="Ma_Y0" fontsize=10];
      Ma_MatMul [shape=box style="filled,rounded" color=orange label="MatMul\n(Ma_MatMul)" fontsize=10];
      X -> Ma_MatMul;
      Ma_MatMulcst -> Ma_MatMul;
      Ma_MatMul -> Ma_Y0;

      Ad_C0 [shape=box label="Ad_C0" fontsize=10];
      Ad_Add [shape=box style="filled,rounded" color=orange label="Add\n(Ad_Add)" fontsize=10];
      Ma_Y0 -> Ad_Add;
      Ad_Addcst -> Ad_Add;
      Ad_Add -> Ad_C0;

      Re_Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Re_Reshape)" fontsize=10];
      Ad_C0 -> Re_Reshape;
      Re_Reshapecst -> Re_Reshape;
      Re_Reshape -> variable;
    }