.. _l-LogisticRegression-~m-cl-dec-liblinear-dec-raw_scores:True,zipmap:False-o17:

LogisticRegression - ~m-cl-dec - liblinear-dec - {'raw_scores': True, 'zipmap': False}
======================================================================================

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

::

        LogisticRegression(n_jobs=8, random_state=0, solver='liblinear')

+----------------------+----------+
| index                | 0        |
+======================+==========+
| skl_nop              | 1        |
+----------------------+----------+
| skl_ncoef            | 3        |
+----------------------+----------+
| skl_nlin             | 1        |
+----------------------+----------+
| onx_size             | 426      |
+----------------------+----------+
| 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       | 426      |
+----------------------+----------+
| onx_nnodes_optim     | 1        |
+----------------------+----------+
| onx_ninits_optim     | 0        |
+----------------------+----------+
| fit_classes_.shape   | 3        |
+----------------------+----------+
| fit_coef_.shape      | (3, 4)   |
+----------------------+----------+
| fit_intercept_.shape | 3        |
+----------------------+----------+
| fit_n_iter_.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=[ 0.45876738  1.29...\nintercepts=[ 0.28357968  0.8646...\nmulti_class=1\npost_transform=b'NONE'" fontsize=10];
      X -> LinearClassifier;
      LinearClassifier -> label;
      LinearClassifier -> probabilities;
    }