Version 0.5

1. New parameters added :

• Scott's pi
• Gwet's AC1
• Bennett S score

2. HTML Report Added (save_html)

Version 0.4

1. New parameters added :

• TPR Micro/Macro
• PPV Micro/Macro
• RACC Overall

2. Normalized Matrix Bugs Fixed
3. Zero Class Bugs Fixed
4. Fleiss & Altman Benchmarks Added
5. Output File(.pycm) Method Added

Version 0.3

1. New parameters added :

• Kappa
• Random Accuracy
• Strength of Agreement

2. Overall statistics added
3. Round bug fixed

Version 0.2

1. New parameters added :

• Population
• Condition positive
• Condition negative
• Test outcome positive
• Test outcome negative
• Prevalence
• G-measure

2. matrix & normalized_matrix method added
3. params method added

Version 0.1

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is usually called a matching matrix). Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa)

pycm(python confusion matrix) is a multi class confusion matrix library in python.

First Release
Supported Class Statistics :

• ACC
• BM
• DOR
• F1-Score
• FDR
• FNR
• FOR
• FPR
• LR+
• LR-
• MCC
• MK
• NPV
• PPV
• TNR
• TPR