Table 1 Prediction performance of the animal-based (ANIM-B) models (CH4 production; g/d) developed using conventional method and four machine learning methods.

From: Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

 

Conventional

glmmLasso

LASSO

SCAD

RF-B

RMSPE

2.96

2.85

2.85

3.00

2.91

Reduction of RMSPE (%)

–

3.80

3.62

-1.25

1.52

MAE

2.29

2.18

2.17

2.29

2.21

Reduction of MAE (%)

–

4.60

5.08

-0.39

3.50

CCC

0.64

0.70

0.71

0.66

0.68

Increase of CCC (%)

–

9.49

9.80

2.64

5.29

  1. *The conventional method only used animal-related data; the relative abundance of all the microbial data was log-transformed; glmmLasso, generalized linear mixed model combined with LASSO; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation implemented on linear mixed models; RF-B, random forest combined with boosting. The data were randomly split into a training set and a testing set (80:20) 200 times and were standardized by mean centering and scaling (detailed in “Methods”).
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