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Table 2 The performance of the proposed pipeline when applied on CRC1 dataset, in terms of mean AUC, precision (P), recall (R) and F-measure (F), and their standard deviation

From: Taxonomy-aware feature engineering for microbiome classification

 

BL

HFE

AUC

P

R

F

AUC

P

R

F

DT

Score

0.635

0.621

0.621

0.620

0.678

0.707

0.676

0.675

std.

0.130

0.150

0.170

0.130

0.103

0.105

0.105

0.096

NB

Score

0.686

0.678

0.676

0.674

0.721

0.715

0.704

0.705

std.

0.110

0.130

0.150

0.110

0.116

0.113

0.102

0.103

RF

Score

0.770

0.677

0.676

0.675

0.795

0.728

0.709

0.706

std.

0.110

0.130

0.150

0.100

0.143

0.125

0.107

0.111

Features

 

count: 18,170

count: 97 std.:12.328