SVC - m-cl - linear - {‘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.svm._classes.SVC'>={'zipmap': False}.

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

index	0
skl_nop	1
skl_ncoef	3
skl_nlin	1
onx_size	1481
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	1481
onx_nnodes_optim	2
onx_ninits_optim	0
fit_class_weight_.shape	3
fit_classes_.shape	3
fit_support_.shape	32
fit_support_vectors_.shape	(32, 4)
fit_dual_coef_.shape	(2, 32)
fit_intercept_.shape	3

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

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SVC - m-cl - poly - {‘zipmap’: False}