LogisticRegression - m-cl - liblinear - {‘zipmap’: False}#
Fitted on a problem type m-cl
(see find_suitable_problem),
method predict_proba matches output .
Model was converted with additional parameter: <class 'sklearn.linear_model._logistic.LogisticRegression'>={'zipmap': False}.
LogisticRegression(n_jobs=8, random_state=0, solver='liblinear')
index |
0 |
|---|---|
skl_nop |
1 |
skl_ncoef |
3 |
skl_nlin |
1 |
onx_size |
523 |
onx_nnodes |
2 |
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 |
1.11.1 |
onx_ai.onnx.ml |
1 |
15 |
|
onx_size_optim |
523 |
onx_nnodes_optim |
2 |
onx_ninits_optim |
0 |
fit_classes_.shape |
3 |
fit_coef_.shape |
(3, 4) |
fit_intercept_.shape |
3 |
fit_n_iter_.shape |
1 |