AdaBoostClassifier - b-cl - default - {‘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.ensemble._weight_boosting.AdaBoostClassifier'>={'zipmap': False}.

AdaBoostClassifier(n_estimators=10, random_state=0)

index

0

skl_nop

2

skl_nnodes

3

skl_ntrees

1

skl_max_depth

1

onx_size

2284

onx_nnodes

23

onx_ninits

8

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_

15

onx_ai.onnx.ml

1

onx_op_Cast

3

onx_op_Reshape

3

onx_size_optim

2284

onx_nnodes_optim

23

onx_ninits_optim

8

fit_estimator_weights_.shape

10

fit_estimator_errors_.shape

10

fit_classes_.shape

2

fit_n_classes_

2

fit_estimators_.size

1

fit_estimators_.sum|tree_.leave_count

2

fit_estimators_.n_features_

4

fit_estimators_.n_classes_

2

fit_estimators_.max|tree_.max_depth

1

fit_estimators_.sum|tree_.node_count

3

fit_estimators_.classes_.shape

2
%0 X X float((0, 4)) TreeEnsembleClassifier TreeEnsembleClassifier (TreeEnsembleClassifier) class_ids=[0 0] class_nodeids=[1 2] class_treeids=[0 0] class_weights=[0. 1.] classlabels_int64s=[0 1] nodes_falsenodeids=[2 0 0] nodes_featureids=[2 0 0] nodes_hitrates=[1. 1. 1.] nodes_missing_value_tracks_true=[0 0 0] nodes_modes=[b'BRANCH_LEQ' b'LE... nodes_nodeids=[0 1 2] nodes_treeids=[0 0 0] nodes_truenodeids=[1 0 0] nodes_values=[2.5489838 0.     ... post_transform=b'NONE' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 2)) ArgMax ArgMax (ArgMax) axis=1 probabilities->ArgMax classes classes int32((2,)) [0 1] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor inverted_n_classes inverted_n_classes float32(()) 0.5 Mul Mul (Mul) inverted_n_classes->Mul n_classes_minus_one n_classes_minus_one float32(()) 1.0 Mul1 Mul (Mul1) n_classes_minus_one->Mul1 Div Div (Div) n_classes_minus_one->Div Mul2 Mul (Mul2) n_classes_minus_one->Mul2 clip_min clip_min float32(()) 2.220446e-16 ClipAda Clip (ClipAda) clip_min->ClipAda axis axis int64((1,)) [1] ReduceSum ReduceSum (ReduceSum) axis->ReduceSum ReduceSum1 ReduceSum (ReduceSum1) axis->ReduceSum1 shape_tensor shape_tensor int64((2,)) [-1  1] Reshape Reshape (Reshape) shape_tensor->Reshape Reshape1 Reshape (Reshape1) shape_tensor->Reshape1 zero_scalar zero_scalar int32(()) 0 Equal Equal (Equal) zero_scalar->Equal shape_tensor2 shape_tensor2 int64((1,)) [-1] Reshape2 Reshape (Reshape2) shape_tensor2->Reshape2 elab_name_0 elab_name_0 eprob_name_0 eprob_name_0 eprob_name_0->ClipAda TreeEnsembleClassifier->elab_name_0 TreeEnsembleClassifier->eprob_name_0 clipped_proba clipped_proba Log Log (Log) clipped_proba->Log ClipAda->clipped_proba log_proba log_proba log_proba->ReduceSum Sub Sub (Sub) log_proba->Sub Log->log_proba reduced_proba reduced_proba reduced_proba->Reshape ReduceSum->reduced_proba reshaped_result reshaped_result reshaped_result->Mul Reshape->reshaped_result prod_result prod_result prod_result->Sub Mul->prod_result sub_result sub_result sub_result->Mul1 Sub->sub_result samme_proba samme_proba Sum Sum (Sum) samme_proba->Sum Mul1->samme_proba summation_prob summation_prob summation_prob->Div Sum->summation_prob div_result div_result div_result->Mul2 Div->div_result exp_operand exp_operand Exp Exp (Exp) exp_operand->Exp Mul2->exp_operand exp_result exp_result exp_result->ReduceSum1 Div1 Div (Div1) exp_result->Div1 Exp->exp_result reduced_exp_result reduced_exp_result reduced_exp_result->Reshape1 ReduceSum1->reduced_exp_result normaliser normaliser Cast Cast (Cast) to=6 normaliser->Cast Add Add (Add) normaliser->Add Reshape1->normaliser cast_normaliser cast_normaliser cast_normaliser->Equal Cast->cast_normaliser comparison_result comparison_result Cast1 Cast (Cast1) to=1 comparison_result->Cast1 Equal->comparison_result cast_output cast_output cast_output->Add Cast1->cast_output zero_filtered_normaliser zero_filtered_normaliser zero_filtered_normaliser->Div1 Add->zero_filtered_normaliser Div1->probabilities argmax_output argmax_output argmax_output->ArrayFeatureExtractor ArgMax->argmax_output array_feature_extractor_result array_feature_extractor_result array_feature_extractor_result->Reshape2 ArrayFeatureExtractor->array_feature_extractor_result reshaped_result1 reshaped_result1 Cast2 Cast (Cast2) to=7 reshaped_result1->Cast2 Reshape2->reshaped_result1 Cast2->label

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