Classification of the Chess Endgame problem using Logistic Regression, Decision Trees, and Neural Networks

TL;DR

This paper compares logistic regression, decision trees, and neural networks for classifying chess endgames, demonstrating neural networks achieve the highest accuracy using Azure ML and showcasing a new visualization tool in Ring language.

Contribution

It evaluates multiple algorithms for chess endgame classification and introduces a new visualization application using the Ring programming language.

Findings

Neural networks achieved 85% accuracy.

Decision trees achieved 79% accuracy.

Azure ML proved effective for quick classification experiments.

Abstract

In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also developed an application for dataset visualization using a new programming language called Ring, our experiments demonstrates that this language have simple design like Python while integrates RAD tools like Visual Basic which is good for GUI development in the open-source world