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Table 16 Bagging and boosting ensemble regressors error metrics result over BSE dataset

From: A comprehensive evaluation of ensemble learning for stock-market prediction

Models

No. of estimators

RMSE

MAE

R2

EVS

MedAE

RMSLE

Train time

Test time

DTBagr

1

0.266

0.213

0.449

0.451

0.210

0.129

0.014

0.004

SVMBagr

1

0.110

0.069

0.906

0.908

0.051

0.051

0.007

0.001

MLPBagr

1

0.061

0.034

0.971

0.972

0.021

0.028

0.141

0.002

DTBotr

1

0.104

0.073

0.916

0.917

0.052

0.046

0.004

0.000

SVMBotr

1

0.081

0.057

0.949

0.949

0.048

0.040

0.005

0.001

MLPBotr

1

0.036

0.026

0.990

0.991

0.023

0.019

0.187

0.001

DTBagr

5

0.091

0.069

0.935

0.935

0.056

0.041

0.015

0.002

SVMBagr

5

0.081

0.057

0.949

0.949

0.046

0.039

0.013

0.003

MLPBagr

5

0.026

0.014

0.995

0.995

0.008

0.013

0.678

0.003

DTBotr

5

0.080

0.060

0.950

0.951

0.047

0.036

0.014

0.001

SVMBotr

5

0.060

0.049

0.972

0.973

0.046

0.030

0.021

0.004

MLPBotr

5

0.027

0.017

0.994

0.995

0.013

0.013

0.799

0.003

DTBagr

15

0.083

0.069

0.946

0.946

0.063

0.039

0.038

0.004

SVMBagr

15

0.076

0.056

0.956

0.956

0.047

0.037

0.034

0.011

MLPBagr

15

0.021

0.014

0.996

0.997

0.010

0.010

3.829

0.006

DTBotr

15

0.069

0.054

0.963

0.963

0.044

0.032

0.096

0.012

SVMBotr

15

0.057

0.047

0.975

0.975

0.043

0.029

0.224

0.039

MLPBotr

15

0.016

0.011

0.998

0.998

0.008

0.007

4.987

0.023

DTBagr

20

0.089

0.075

0.939

0.939

0.072

0.042

0.068

0.006

SVMBagr

20

0.075

0.055

0.956

0.956

0.044

0.037

0.040

0.013

MLPBagr

20

0.021

0.014

0.996

0.997

0.010

0.010

4.038

0.021

DTBotr

20

0.077

0.057

0.954

0.955

0.044

0.033

0.058

0.006

SVMBotr

20

0.057

0.047

0.975

0.976

0.043

0.028

0.089

0.020

MLPBotr

20

0.014

0.010

0.999

0.999

0.008

0.006

4.824

0.009

DTBagr

50

0.078

0.064

0.953

0.953

0.057

0.037

0.114

0.010

SVMBagr

50

0.072

0.054

0.960

0.960

0.044

0.036

0.226

0.056

MLPBagr

50

0.014

0.010

0.999

0.999

0.007

0.007

13.967

0.021

DTBotr

50

0.047

0.038

0.983

0.983

0.033

0.023

0.211

0.010

SVMBotr

50

0.056

0.047

0.975

0.976

0.044

0.028

0.186

0.039

MLPBotr

50

0.013

0.009

0.999

0.999

0.006

0.006

22.515

0.019

DTBagr

100

0.075

0.061

0.956

0.956

0.055

0.036

0.325

0.045

SVMBagr

100

0.073

0.054

0.959

0.959

0.045

0.036

0.559

0.127

MLPBagr

100

0.014

0.010

0.999

0.999

0.008

0.007

34.135

0.053

DTBotr

100

0.039

0.032

0.988

0.988

0.029

0.020

0.315

0.021

SVMBotr

100

0.056

0.047

0.975

0.976

0.044

0.028

0.787

0.357

MLPBotr

100

0.011

0.008

0.999

0.999

0.006

0.005

28.182

0.039

DTBagr

150

0.073

0.059

0.959

0.959

0.053

0.035

0.339

0.042

SVMBagr

150

0.072

0.054

0.960

0.960

0.045

0.036

0.401

0.119

MLPBagr

150

0.013

0.010

0.999

0.999

0.008

0.007

30.237

0.082

DTBotr

150

0.038

0.031

0.989

0.990

0.028

0.019

0.345

0.024

SVMBotr

150

0.056

0.047

0.975

0.976

0.044

0.028

0.115

0.019

MLPBotr

150

0.011

0.008

0.999

0.999

0.006

0.005

24.404

0.187

DTBagr

200

0.073

0.060

0.958

0.958

0.055

0.035

0.786

0.043

SVMBagr

200

0.071

0.054

0.960

0.961

0.044

0.035

0.487

0.100

MLPBagr

200

0.013

0.010

0.999

0.999

0.008

0.007

38.432

0.091

DTBotr

200

0.035

0.028

0.990

0.991

0.024

0.018

0.453

0.027

SVMBotr

200

0.056

0.047

0.975

0.976

0.044

0.028

0.126

0.021

MLPBotr

200

0.011

0.008

0.999

0.999

0.006

0.005

24.178

0.039