GridSearchCV - ~b-cl-64 - cl - {‘zipmap’: False}#

Fitted on a problem type ~b-cl-64 (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

1095

onx_nnodes

13

onx_ninits

5

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_

14

onx_ai.onnx.ml

1

onx_op_Cast

2

onx_op_Identity

2

onx_op_Reshape

1

onx_size_optim

998

onx_nnodes_optim

11

onx_ninits_optim

5

fit_best_score_.shape

1
%0 X X double((0, 4)) MatMul MatMul (MatMul) X->MatMul label label int64((0,)) probabilities probabilities double((0, 2)) coef coef float64((4, 2)) [[ 0.49765062 -0.49765062] [ 1.31840369 -1.31840369] [-2.30954617  2.30954617] [-0.69495593  0.69495593]] coef->MatMul intercept intercept float64((1, 2)) [[-0.  0.]] Add Add (Add) intercept->Add classes classes int32((2,)) [0 1] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape axis axis int64((1,)) [1] ReduceSum ReduceSum (ReduceSum) keepdims=1 axis->ReduceSum multiplied multiplied multiplied->Add MatMul->multiplied raw_scores raw_scores ArgMax ArgMax (ArgMax) axis=1 raw_scores->ArgMax Sigmoid Sigmoid (Sigmoid) raw_scores->Sigmoid Add->raw_scores label2 label2 label2->ArrayFeatureExtractor ArgMax->label2 raw_scoressig raw_scoressig Abs Abs (Abs) raw_scoressig->Abs NormalizerNorm Div (NormalizerNorm) raw_scoressig->NormalizerNorm Sigmoid->raw_scoressig norm_abs norm_abs norm_abs->ReduceSum Abs->norm_abs array_feature_extractor_result array_feature_extractor_result Cast Cast (Cast) to=11 array_feature_extractor_result->Cast ArrayFeatureExtractor->array_feature_extractor_result norm norm norm->NormalizerNorm ReduceSum->norm cast2_result cast2_result cast2_result->Reshape Cast->cast2_result probabilities1 probabilities1 Identity1 Identity (Identity1) probabilities1->Identity1 NormalizerNorm->probabilities1 reshaped_result reshaped_result Cast1 Cast (Cast1) to=7 reshaped_result->Cast1 Reshape->reshaped_result Identity1->probabilities label1 label1 Identity Identity (Identity) label1->Identity Cast1->label1 Identity->label

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