.. _l-RidgeClassifier-~m-cl-nop-default-zipmap:False-o17:

RidgeClassifier - ~m-cl-nop - default - {'zipmap': False}
=========================================================

Fitted on a problem type *~m-cl-nop*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict` matches output .
Model was converted with additional parameter: ``<class 'sklearn.linear_model._ridge.RidgeClassifier'>={'zipmap': False}``.

::

        RidgeClassifier(random_state=0)

+----------------------+----------+
| index                | 0        |
+======================+==========+
| skl_nop              | 1        |
+----------------------+----------+
| skl_ncoef            | 3        |
+----------------------+----------+
| skl_nlin             | 1        |
+----------------------+----------+
| onx_size             | 423      |
+----------------------+----------+
| 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       | 423      |
+----------------------+----------+
| onx_nnodes_optim     | 1        |
+----------------------+----------+
| onx_ninits_optim     | 0        |
+----------------------+----------+
| fit_coef_.shape      | (3, 4)   |
+----------------------+----------+
| fit_intercept_.shape | 3        |
+----------------------+----------+


.. 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];


      LinearClassifier [shape=box style="filled,rounded" color=orange label="LinearClassifier\n(LinearClassifier)\nclasslabels_ints=[0 1 2]\ncoefficients=[ 0.10896494  0.33...\nintercepts=[-0.21448171  1.8669...\nmulti_class=0\npost_transform=b'NONE'" fontsize=10];
      X -> LinearClassifier;
      LinearClassifier -> label;
      LinearClassifier -> probabilities;
    }