# Entropy Triangle Weka package

A set of information-theoretic tools for the assessment of multi-class classifiers in Weka

## Features

**Visualization Plugin**with an**exploratory data analysis method**for an easy, but complete and reliable comparison of classifiers performance.The Entropy Triangle is based on a balance equation of entropies. Represents normalized values of the variation of information, the mutual information, and the increment of entropy from the uniform distribution in a ternary plot. This lets you analyze at a glance different scenarios without loss of information.

- Panel with
**colorbar**(or labels) for the chosen metric (or classifier/dataset information) **Tooltips**with detailed information hovering graph elements- Delete elements of the graph with a
*right-click*context menu **Save and load graph data**in arff format**Export data to csv or json****Print the graph**with*Ctrl+Shft+Alt+Left Mouse Click*

- Panel with
**New evaluation metrics**for the standard output of Weka.Entropy Modulated Accuracy (EMA)

The EMA is a pessimistic estimate of the accuracy with the influence of the input distribution factored out. It is defined mathematically as the inverse of the remaining perplexity after the learning process

$$ EMA = \frac{1}{PP_{X|Y}} = \frac{1}{2^{H_{X|Y}}} $$

Normalized Information Transfer factor (NIT factor).

A measure of how efficient is the transmission of information from the input to the output of the classifier

$$ NIT_{factor} = \frac{\mu_{XY}}{k} $$ where

*k*is the number of classes.Optional

**advanced information-theoretic metrics**.Class perplexity

The class perplexity can be interpreted as the number of effective classes at the input of the classifier, due to the likelihood of the input distribution

$$ PP_X = 2^{H_X} $$

Remaining perplexity

The Remaining Perplexity measures the residual difficulty after the learning process $$ PP_{X|Y} = 2^{H_{X|Y}} $$

Information Transfer factor

The Information Transfer factor measures the variation in difficulty due to the mutual information between the input and the output distributions of the classifier. $$ \mu_{XY} = 2^{MI_{XY}} $$

Variation of information $$ VI = H_{X|Y} + H_{Y|X} $$

## Theoretical background

This package is an implementation of the work of these papers: