Description: PyCM is a multi-class confusion matrix library written in Python. http://www.pycm.io

# PyCM Report

## Dataset Type :

- Balanced
- Multi-Class Classification

Note 1 : Recommended statistics for this type of classification highlighted in aqua

Note 2 : The recommendation system assumes the input is the result of classification over the entire dataset, not just a subset. If the confusion matrix is based on test data classification, the recommendation may not be valid.

## Confusion Matrix :

Actual | Predict
| L1 | L2 | L3
L1 | 3 | 0 | 2
L2 | 0 | 1 | 1
L3 | 0 | 2 | 3

## Overall Statistics :

95% CI | (0.30439,0.86228)
ACC Macro | 0.72222
ARI | 0.09206
AUNP | 0.68571
AUNU | 0.67857
Bangdiwala B | 0.37255
Bennett S | 0.375
CBA | 0.47778
CSI | 0.17778
Chi-Squared | 6.6
Chi-Squared DF | 4
Conditional Entropy | 0.97579
Cramer V | 0.5244
Cross Entropy | 1.58333
F1 Macro | 0.56515
F1 Micro | 0.58333
FNR Macro | 0.43333
FNR Micro | 0.41667
FPR Macro | 0.20952
FPR Micro | 0.20833
Gwet AC1 | 0.38931
Hamming Loss | 0.41667
Joint Entropy | 2.45915
KL Divergence | 0.09998
Kappa | 0.35484
Kappa 95% CI | (-0.07708,0.78675)
Kappa No Prevalence | 0.16667
Kappa Standard Error | 0.22036
Kappa Unbiased | 0.34426
Krippendorff Alpha | 0.37158
Lambda A | 0.42857
Lambda B | 0.16667
Mutual Information | 0.52421
NIR | 0.41667
NPV Macro | 0.77778
NPV Micro | 0.79167
Overall ACC | 0.58333
Overall CEN | 0.46381
Overall J | (1.225,0.40833)
Overall MCC | 0.36667
Overall MCEN | 0.51894
Overall RACC | 0.35417
Overall RACCU | 0.36458
P-Value | 0.18926
PPV Macro | 0.61111
PPV Micro | 0.58333
Pearson C | 0.59568
Phi-Squared | 0.55
RCI | 0.35339
RR | 4.0
Reference Entropy | 1.48336
Response Entropy | 1.5
SOA1(Landis & Koch) | Fair
SOA2(Fleiss) | Poor
SOA3(Altman) | Fair
SOA4(Cicchetti) | Poor
SOA5(Cramer) | Relatively Strong
SOA6(Matthews) | Weak
SOA7(Lambda A) | Moderate
SOA8(Lambda B) | Very Weak
SOA9(Krippendorff Alpha) | Low
SOA10(Pearson C) | Strong
Scott PI | 0.34426
Standard Error | 0.14232
TNR Macro | 0.79048
TNR Micro | 0.79167
TPR Macro | 0.56667
TPR Micro | 0.58333
Zero-one Loss | 5

## Class Statistics :

Class | L1 | L2 | L3 | Description
ACC | 0.83333 | 0.75 | 0.58333 | Accuracy
AGF | 0.72859 | 0.62869 | 0.61009 | Adjusted F-score
AGM | 0.85764 | 0.70861 | 0.58034 | Adjusted geometric mean
AM | -2 | 1 | 1 | Difference between automatic and manual classification
AUC | 0.8 | 0.65 | 0.58571 | Area under the ROC curve
AUCI | Very Good | Fair | Poor | AUC value interpretation
AUPR | 0.8 | 0.41667 | 0.55 | Area under the PR curve
BB | 0.6 | 0.33333 | 0.5 | Braun-Blanquet similarity
BCD | 0.08333 | 0.04167 | 0.04167 | Bray-Curtis dissimilarity
BM | 0.6 | 0.3 | 0.17143 | Informedness or bookmaker informedness
CEN | 0.25 | 0.49658 | 0.60442 | Confusion entropy
DOR | None | 4.0 | 2.0 | Diagnostic odds ratio
DP | None | 0.33193 | 0.16597 | Discriminant power
DPI | None | Poor | Poor | Discriminant power interpretation
ERR | 0.16667 | 0.25 | 0.41667 | Error rate
F0.5 | 0.88235 | 0.35714 | 0.51724 | F0.5 score
F1 | 0.75 | 0.4 | 0.54545 | F1 score - harmonic mean of precision and sensitivity
F2 | 0.65217 | 0.45455 | 0.57692 | F2 score
FDR | 0.0 | 0.66667 | 0.5 | False discovery rate
FN | 2 | 1 | 2 | False negative/miss/type 2 error
FNR | 0.4 | 0.5 | 0.4 | Miss rate or false negative rate
FOR | 0.22222 | 0.11111 | 0.33333 | False omission rate
FP | 0 | 2 | 3 | False positive/type 1 error/false alarm
FPR | 0.0 | 0.2 | 0.42857 | Fall-out or false positive rate
G | 0.7746 | 0.40825 | 0.54772 | G-measure geometric mean of precision and sensitivity
GI | 0.6 | 0.3 | 0.17143 | Gini index
GM | 0.7746 | 0.63246 | 0.58554 | G-mean geometric mean of specificity and sensitivity
HD | 2 | 3 | 5 | Hamming distance
IBA | 0.36 | 0.28 | 0.35265 | Index of balanced accuracy
ICSI | 0.6 | -0.16667 | 0.1 | Individual classification success index
IS | 1.26303 | 1.0 | 0.26303 | Information score
J | 0.6 | 0.25 | 0.375 | Jaccard index
LS | 2.4 | 2.0 | 1.2 | Lift score
MCC | 0.68313 | 0.2582 | 0.16903 | Matthews correlation coefficient
MCCI | Moderate | Negligible | Negligible | Matthews correlation coefficient interpretation
MCEN | 0.26439 | 0.5 | 0.6875 | Modified confusion entropy
MK | 0.77778 | 0.22222 | 0.16667 | Markedness
N | 7 | 10 | 7 | Condition negative
NLR | 0.4 | 0.625 | 0.7 | Negative likelihood ratio
NLRI | Poor | Negligible | Negligible | Negative likelihood ratio interpretation
NPV | 0.77778 | 0.88889 | 0.66667 | Negative predictive value
OC | 1.0 | 0.5 | 0.6 | Overlap coefficient
OOC | 0.7746 | 0.40825 | 0.54772 | Otsuka-Ochiai coefficient
OP | 0.58333 | 0.51923 | 0.55894 | Optimized precision
P | 5 | 2 | 5 | Condition positive or support
PLR | None | 2.5 | 1.4 | Positive likelihood ratio
PLRI | None | Poor | Poor | Positive likelihood ratio interpretation
POP | 12 | 12 | 12 | Population
PPV | 1.0 | 0.33333 | 0.5 | Precision or positive predictive value
PR | 0.41667 | 0.16667 | 0.41667 | Positive rate
PRE | 0.41667 | 0.16667 | 0.41667 | Prevalence
Q | None | 0.6 | 0.33333 | Yule Q - coefficient of colligation
QI | None | Moderate | Weak | Yule Q interpretation
RACC | 0.10417 | 0.04167 | 0.20833 | Random accuracy
RACCU | 0.11111 | 0.0434 | 0.21007 | Random accuracy unbiased
TN | 7 | 8 | 4 | True negative/correct rejection
TNR | 1.0 | 0.8 | 0.57143 | Specificity or true negative rate
TON | 9 | 9 | 6 | Test outcome negative
TOP | 3 | 3 | 6 | Test outcome positive
TOPR | 0.25 | 0.25 | 0.5 | Test outcome positive rate
TP | 3 | 1 | 3 | True positive/hit
TPR | 0.6 | 0.5 | 0.6 | Sensitivity, recall, hit rate, or true positive rate
Y | 0.6 | 0.3 | 0.17143 | Youden index
dInd | 0.4 | 0.53852 | 0.58624 | Distance index
sInd | 0.71716 | 0.61921 | 0.58547 | Similarity index

Generated By PyCM Version 4.6