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

# PyCM Report

## Dataset Type :

- Multi-Class Classification
- Balanced

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 (Normalized):

Actual | Predict
| L1 | L2 | L3
L1 | 0.6 | 0.0 | 0.4
L2 | 0.0 | 0.5 | 0.5
L3 | 0.0 | 0.4 | 0.6

## Overall Statistics :

ACC Macro | 0.72222
F1 Macro | 0.56515
FPR Macro | 0.20952
Kappa | 0.35484
NPV Macro | 0.77778
Overall ACC | 0.58333
PPV Macro | 0.61111
SOA1(Landis & Koch) | Fair
TPR Macro | 0.56667
Zero-one Loss | 5

## Class Statistics :

Class | L1 | L2 | L3 | Description
ACC | 0.83333 | 0.75 | 0.58333 | Accuracy
AUC | 0.8 | 0.65 | 0.58571 | Area under the ROC curve
AUCI | Very Good | Fair | Poor | AUC value interpretation
F1 | 0.75 | 0.4 | 0.54545 | F1 score - harmonic mean of precision and sensitivity
TPR | 0.6 | 0.5 | 0.6 | Sensitivity, recall, hit rate, or true positive rate
FPR | 0.0 | 0.2 | 0.42857 | Fall-out or false positive rate
PPV | 1.0 | 0.33333 | 0.5 | Precision or positive predictive value
TP | 3 | 1 | 3 | True positive/hit
FP | 0 | 2 | 3 | False positive/type 1 error/false alarm
FN | 2 | 1 | 2 | False negative/miss/type 2 error
TN | 7 | 8 | 4 | True negative/correct rejection
N | 7 | 10 | 7 | Condition negative
P | 5 | 2 | 5 | Condition positive or support
POP | 12 | 12 | 12 | Population
TOP | 3 | 3 | 6 | Test outcome positive
TON | 9 | 9 | 6 | Test outcome negative

Generated By PyCM Version 4.2