Skip to main content

Table 3 Predictive performance on WPBC dataset

From: Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research

Dataset

Model

KNN

RF

SVR

Ridge

Lasso

MTLasso

MLP-4

GATAN

WPBC

MSE

1139.06

1189.69

1007.50

1184.38

1000.94

990.63

941.88

860.63

  

(200.05)

(273.34)

(153.95)

(253.02)

(144.57)

(163.17)

(145.68)

(65.49)

 

EVS

-0.22

-0.17

0.00

-0.21

-0.01

0.01

0.00

-0.01

  

(0.15)

(0.17)

(0.01)

(0.28)

(0.15)

(0.14)

(0.01)

(0.02)

 

MAE

25.48

28.00

27.16

27.78

24.58

24.16

27.09

23.86

  

(4.94)

(3.31)

(6.08)

(4.27)

(1.32)

(3.30)

(4.88)

(0.79)

  1. The best performance is bolded