Log In Sign Up
Models:
Daniel Galbraith
DanielG/
BioDiscML
0
Downloads: 1
[418bf5]: / benchmark / prostate-singh / prostateSingh_d.misc.VFI_-B0.4_ACC_BF.details.txt

Download this file

101 lines (89 with data), 2.1 kB 

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
## Generated by BioDiscML (Leclercq et al. 2018)##

# Project: prostateSingh

# ID: misc.VFI_ACC_BF_12_1_451

# Classifier: misc.VFI  -B 0.4

# Optimizer: ACC

# Feature search mode: BF



# 10 fold cross validation performance

ACC: 1

AUC: 1

AUPRC: 1

TPR: 1

TNR: 1

MCC: 1

BER: 0

FPR: 0

FNR: 0

PPV: 1

FDR: 0

recall: 1

Fscore: 1

kappa: 1

matrix: [50 0] [0 52] 



# LOOCV (Leave-One-Out Cross Validation) performance

ACC: 0.9902

AUC: 1

AUPRC: 1

TPR: 0.9902

TNR: 0.9906

MCC: 0.9806

BER: 0.0096

FPR: 0.0094

FNR: 0.0098

PPV: 0.9904

FDR: 0.0096

recall: 0.9902

Fscore: 0.9902

kappa: 0.9804

matrix: [50 0] [1 51] 



#Repeated Holdout evaluation performance on TRAIN set, 100 times weighted average (and standard deviation) on random seeds

Average weighted ACC: 0.929

Average weighted AUC: 0.954

Average weighted AUPRC: 0.953

Average weighted SEN: 0.929

Average weighted SPE: 0.93

Average weighted MCC: 0.863

Average weighted BER: 0.071



#Bootstrap evaluation performance on TRAIN set, 100 times weighted average (and standard deviation) on random seeds

Average weighted ACC: 0.927

Average weighted AUC: 0.947

Average weighted AUPRC: 0.944

Average weighted SEN: 0.927

Average weighted SPE: 0.929

Average weighted MCC: 0.858

Average weighted BER: 0.072



#Bootstrap .632+ rule calculated on TRAIN set, 100 folds with random seeds

0.063



# Selected Attributes	(Total attributes:12). Occurrences are shown if you chose combined model

1944_f_at

216_at

31427_at

31687_f_at

32269_at

34310_at

35773_i_at

36629_at

36635_at

39637_at

40783_s_at

41430_at



# Attribute ranking by merit calculated by information gain

0.3628	216_at 

0.1328	36629_at 

0.1283	34310_at 

0.1167	40783_s_at 

0.1167	35773_i_at 

0.1139	41430_at 

0.1089	36635_at 

0.1027	31427_at 

0.0972	39637_at 

0.0972	1944_f_at 

0.0972	31687_f_at 

0.0972	32269_at 



# Correlated features (Spearman)

FeatureInSignature	SpearmanCorrelationScore	CorrelatedFeature

216_at	0.974	38406_f_at



# Correlated features (Pearson)

FeatureInSignature	SpearmanCorrelationScore	CorrelatedFeature

216_at	0.954	38406_f_at





## End of file ##