GridSearchCV - m-cl - cl - {‘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.model_selection._search.GridSearchCV'>={'zipmap': False}.

GridSearchCV(estimator=LogisticRegression(random_state=0, solver='liblinear'),
         n_jobs=1, param_grid={'fit_intercept': [False, True]})

index

0

skl_nop

1

onx_size

614

onx_nnodes

4

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

onx_

14

onx_op_Identity

2

onx_size_optim

517

onx_nnodes_optim

2

onx_ninits_optim

0

fit_best_score_.shape

1

%0 X X float((0, 4)) LinearClassifier LinearClassifier (LinearClassifier) classlabels_ints=[0 1 2] coefficients=[ 0.45876738  1.29... intercepts=[ 0.28357968  0.8646... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier label label int64((0,)) probabilities probabilities float((0, 3)) label1 label1 Identity Identity (Identity) label1->Identity probability_tensor probability_tensor Normalizer Normalizer (Normalizer) norm=b'L1' probability_tensor->Normalizer LinearClassifier->label1 LinearClassifier->probability_tensor probabilities1 probabilities1 Identity1 Identity (Identity1) probabilities1->Identity1 Normalizer->probabilities1 Identity->label Identity1->probabilities