VotingRegressor - b-reg - linreg -#

Fitted on a problem type b-reg (see find_suitable_problem), method predict matches output .

VotingRegressor(estimators=[('lr1', LinearRegression()),
                        ('lr2', LinearRegression(fit_intercept=False))],
            n_jobs=8)

index

0

skl_nop

3

skl_ncoef

8

skl_nlin

2

onx_size

564

onx_nnodes

7

onx_ninits

1

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_size_optim

564

onx_nnodes_optim

7

onx_ninits_optim

1

fit_estimators_.size

2

fit_estimators_.singular_.shape

4

fit_estimators_.coef_.shape

4

%0 X X float((0, 4)) LinearRegressor1 LinearRegressor (LinearRegressor1) coefficients=[-0.16883491  0.06... intercepts=[0.] X->LinearRegressor1 LinearRegressor LinearRegressor (LinearRegressor) coefficients=[-0.19476996  0.04... intercepts=[0.17583406] X->LinearRegressor variable variable float((0, 1)) w0 w0 float32((1,)) [0.5] Mul Mul (Mul) w0->Mul Mul1 Mul (Mul1) w0->Mul1 var_1 var_1 var_1->Mul1 LinearRegressor1->var_1 var_0 var_0 var_0->Mul LinearRegressor->var_0 wvar_0 wvar_0 N1 Flatten (N1) wvar_0->N1 Mul->wvar_0 wvar_1 wvar_1 N4 Flatten (N4) wvar_1->N4 Mul1->wvar_1 fvar_1 fvar_1 Sum Sum (Sum) fvar_1->Sum N4->fvar_1 fvar_0 fvar_0 fvar_0->Sum N1->fvar_0 Sum->variable