VotingClassifier - m-cl - logreg-noflatten - {‘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.ensemble._voting.VotingClassifier'>={'zipmap': False}.
VotingClassifier(estimators=[('lr1', LogisticRegression(solver='liblinear')),
('lr2',
LogisticRegression(fit_intercept=False,
solver='liblinear'))],
flatten_transform=False, n_jobs=8, voting='soft')
index |
0 |
|---|---|
skl_nop |
3 |
skl_ncoef |
6 |
skl_nlin |
2 |
onx_size |
1498 |
onx_nnodes |
12 |
onx_ninits |
4 |
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 |
15 |
|
onx_ai.onnx.ml |
1 |
onx_op_Cast |
2 |
onx_op_Reshape |
1 |
onx_size_optim |
1472 |
onx_nnodes_optim |
12 |
onx_ninits_optim |
3 |
fit_classes_.shape |
3 |
fit_estimators_.size |
2 |
fit_estimators_.intercept_.shape |
3 |
fit_estimators_.coef_.shape |
(3, 4) |
fit_estimators_.n_iter_.shape |
1 |
fit_estimators_.classes_.shape |
3 |