SVC - b-cl - sigmoid - {‘zipmap’: False}#

Fitted on a problem type b-cl (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.svm._classes.SVC'>={'zipmap': False}.

SVC(kernel='sigmoid', probability=True, random_state=0)

index

0

skl_nop

1

onx_size

2238

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

onx_

9

onx_op_Cast

1

onx_size_optim

2238

onx_nnodes_optim

2

onx_ninits_optim

0

fit_class_weight_.shape

2

fit_classes_.shape

2

fit_support_.shape

70

fit_support_vectors_.shape

(70, 4)

fit_dual_coef_.shape

(1, 70)

fit_intercept_.shape

1
%0 X X float((0, 4)) SVMc SVMClassifier (SVMc) classlabels_ints=[0 1] coefficients=[ 1.  1.  1.  1.  ... kernel_params=[0.06311981 0.   ... kernel_type=b'SIGMOID' post_transform=b'NONE' prob_a=[12.142247] prob_b=[14.385983] rho=[-0.8328141] support_vectors=[ 4.3017502e+00... vectors_per_class=[35 35] X->SVMc label label int64((0,)) probabilities probabilities float((0, 2)) SVM02 SVM02 Cast Cast (Cast) to=1 SVM02->Cast SVMc->label SVMc->SVM02 Cast->probabilities

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SVC - b-cl - rbf - {‘zipmap’: False}

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SVC - m-cl - linear - {‘zipmap’: False}