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Table 3 Impurity-based feature selection using Random Forest for predicting \(PA_{f}\) (a) and \(WAI_{f}\) (b)

From: Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

\(PA_{f}\)

\(WAI_{f}\)

Model

MAE ± SD

\({R}^2\)

MR

Model

MAE ± SD

\({R}^2\)

MR

LR

1.54 ± 1.18

0.25

0.050

LR

1.16 ± 1.12

0.27

0.003

PAR

1.54 ± 1.19

0.25

− 0.087

PAR

1.10 ± 1.14

0.28

− 0.288

SGDR

1.55 ± 1.17

0.25

0.143

SGDR

1.10 ± 1.13

0.29

− 0.243

RFR

1.57 ± 1.13

0.25

0.199

RFR

1.09 ± 1.20

0.25

− 0.246

ABR

1.60 ± 1.14

0.23

0.0

ABR

1.21 ± 1.20

0.18

− 0.090

SVR

1.53 ± 1.15

0.27

0.102

SVR

1.11 ± 1.15

0.27

− 0.221

XGB

1.55 ± 1.13

0.26

− 0.015

XGB

1.18 ± 1.12

0.25

0.016

  1. The best performing model are highlighted in bold letters