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Table 2 Final results for the classification of mystery samples from mystery set 1 and 3

From: Identification of city specific important bacterial signature for the MetaSUB CAMDA challenge microbiome data

Sample

Random Forest

SVM

Final Prediction

Real Label

Status of the prediction

Prediction

Score

Departures

Adj. Score

Prediction

Score

Departures

Adj. Score

Mystery Set 1

 C1.001

SAC

1.000

0

1.000

SAC

0.848

2

0.240

SAC

SCL

 

 C1.002

SCL

1.000

0

1.000

SCL

1.000

0

1.000

SCL

SCL

CORRECT

 C1.003

NYC

1.000

0

1.000

NYC

0.771

1

0.297

NYC

OFA

 

 C1.004

PXO

1.000

0

1.000

PXO

1.000

1

0.500

PXO

PXO

CORRECT

 C1.005

NYC

1.000

0

1.000

OFA

1.000

1

0.500

NYC

OFA

 

 C1.006

PXO

0.999

1

0.499

PXO

0.821

3

0.168

PXO

PXO

CORRECT

 C1.007

SCL

0.971

1

0.471

SCL

0.769

1

0.296

SCL

SCL

CORRECT

 C1.008

PXO

1.000

0

1.000

PXO

0.696

3

0.121

PXO

PXO

CORRECT

 C1.009

NYC

1.000

0

1.000

OFA

0.619

1

0.192

NYC

NYC

CORRECT

 C1.010

PXO

1.000

0

1.000

PXO

0.698

2

0.162

PXO

PXO

CORRECT

 C1.011

SCL

1.000

0

1.000

SCL

0.741

4

0.110

SCL

SCL

CORRECT

 C1.012

OFA

1.000

0

1.000

OFA

1.000

0

1.000

OFA

OFA

CORRECT

 C1.013

PXO

1.000

0

1.000

PXO

0.864

2

0.249

PXO

PXO

CORRECT

 C1.014

SAC

1.000

0

1.000

SCL

0.717

2

0.171

SAC

SCL

 

 C1.015

TOK

1.000

0

1.000

HAM

0.462

3

0.053

TOK

NYC

 

 C1.016

OFA

0.913

1

0.416

NYC

0.826

1

0.341

OFA

NYC

 

 C1.017

SCL

0.610

1

0.186

TOK

0.543

3

0.074

SCL

PXO

 

 C1.018

NYC

1.000

0

1.000

NYC

0.995

1

0.495

NYC

NYC

CORRECT

 C1.019

AKL

1.000

0

1.000

OFA

1.000

0

1.000

Inconclusive

NYC

 

 C1.020

OFA

1.000

0

1.000

OFA

1.000

1

0.500

OFA

OFA

CORRECT

 C1.021

AKL

0.834

3

0.174

OFA

0.997

3

0.248

OFA

NYC

 

 C1.022

PXO

1.000

0

1.000

PXO

0.894

1

0.399

PXO

PXO

CORRECT

 C1.023

NYC

1.000

1

0.500

NYC

0.990

2

0.327

NYC

NYC

CORRECT

 C1.024

NYC

0.852

1

0.363

NYC

0.898

4

0.161

NYC

NYC

CORRECT

 C1.025

NYC

1.000

0

1.000

NYC

0.997

4

0.199

NYC

NYC

CORRECT

 C1.026

PXO

1.000

0

1.000

PXO

1.000

0

1.000

PXO

PXO

CORRECT

 C1.027

PXO

1.000

0

1.000

TOK

0.621

1

0.193

PXO

PXO

CORRECT

 C1.028

OFA

1.000

0

1.000

OFA

1.000

0

1.000

OFA

OFA

CORRECT

 C1.029

AKL

1.000

1

0.500

NYC

0.494

3

0.061

AKL

NYC

 

 C1.030

TOK

0.761

1

0.290

TOK

0.994

1

0.494

TOK

PXO

 

Mystery Set 3

 C5.001

Boston

1.000

0

1.000

Boston

1.000

0

1.000

Boston

Boston

CORRECT

 C5.002

Ilorin

1.000

0

1.000

Lisbon

0.754

1

0.284

Ilorin

Ilorin

CORRECT

 C5.003

Lisbon

1.000

0

1.000

Lisbon

1.000

0

1.000

Lisbon

Lisbon

CORRECT

 C5.004

Ilorin

1.000

0

1.000

Ilorin

0.568

1

0.161

Ilorin

Ilorin

CORRECT

 C5.005

Lisbon

1.000

0

1.000

Lisbon

0.999

1

0.499

Lisbon

Lisbon

CORRECT

 C5.006

Lisbon

1.000

0

1.000

Ilorin

0.616

1

0.190

Lisbon

Ilorin

 

 C5.007

Boston

1.000

0

1.000

Lisbon

0.749

1

0.280

Boston

Bogota

 

 C5.008

Lisbon

0.999

1

0.499

Lisbon

0.772

1

0.298

Lisbon

Bogota

 

 C5.009

Lisbon

1.000

0

1.000

Lisbon

1.000

0

1.000

Lisbon

Lisbon

CORRECT

 C5.010

Ilorin

0.384

2

0.049

Boston

0.982

1

0.482

Boston

Bogota

 

 C5.011

Lisbon

1.000

0

1.000

Lisbon

1.000

0

1.000

Lisbon

Bogota

 

 C5.012

Lisbon

1.000

0

1.000

Lisbon

0.988

1

0.488

Lisbon

Lisbon

CORRECT

 C5.013

Boston

1.000

0

1.000

Boston

0.998

1

0.498

Boston

Boston

CORRECT

 C5.014

Ilorin

1.000

0

1.000

Ilorin

1.000

0

1.000

Ilorin

Ilorin

CORRECT

 C5.015

Boston

1.000

0

1.000

Lisbon

0.750

1

0.282

Boston

Boston

CORRECT

 C5.016

Boston

0.843

1

0.356

Lisbon

0.750

1

0.282

Boston

Bogota

 
  1. Table shows samples abbreviated names, partial results from both classifiers (RF and SVM) and voted results, actual label of each sample, and whether the samples prediction was correct. Results for sample C1.019 were not correct but also labeled as inconclusive since both classifiers predicted a different city with the same adjusted score. Additionally, in similar cases whether or not one of the classifiers was correct or not was irrelevant due to the inability of the pipeline to produce a label