Table 5 External validation of the models.

From: Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand

`

0.85 < k < 1.15

0.85 < k′ < 1.15

R2 \(\cong {1}\)

\({\text{R}}_{o}^{2}\cong {1}\)

Rm > 0.5

m < 0.1

CS

Ā SVR

0.995

1.101

0.995

0.992

0.926

āˆ’Ā 0.005

Ā DT

0.984

1.007

0.998

0.995

0.948

0.003

Ā AR

0.999

1.001

0.999

0.999

0.978

āˆ’Ā 0.0005

Ā SVR-FFA

0.987

1.006

0.997

0.994

0.913

āˆ’Ā 0.004

Ā SVR-PSO

0.986

1.012

0.995

0.996

0.943

āˆ’Ā 0.004

Ā SVR-GWO

0.999

1.000

0.999

0.999

0.989

āˆ’Ā 0.0004

E

Ā SVR

0.993

1.002

0.990

0.999

0.896

āˆ’Ā 0.009

Ā DT

0.988

1.004

0.999

0.999

0.946

āˆ’Ā 0.00001

Ā AR

0.998

1.001

0.999

0.999

0.999

0.00002

Ā SVR-FFA

0.997

0.987

0.993

0.999

0.933

āˆ’Ā 0.00001

Ā SVR-PSO

0.997

0.989

0.994

0.999

0.954

āˆ’Ā 0.0008

Ā SVR-GWO

0.999

1.000

1.001

1.001

1.000

0.00001

STS

Ā SVR

0.946

1.049

0.849

0.861

0.762

āˆ’Ā 0.012

Ā DT

0.978

1.009

0.966

0.994

0.803

āˆ’Ā 0.029

Ā AR

0.997

1.000

0.997

0.999

0.942

āˆ’Ā 0.003

Ā SVR-FFA

0.986

1.004

0.974

0.993

0.892

āˆ’Ā 0.005

Ā SVR-PSO

0.985

1.007

0.982

0.992

0.912

āˆ’Ā 0.004

Ā SVR-GWO

0.998

1.000

0.999

0.999

0.976

āˆ’Ā 0.0002