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

arXiv: https://arxiv.org/abs/2111.05976 · HTML: https://mlsanity.com/paper/2111.05976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [PWCT: Visual Language for IoT and Cloud Computing Applications and Systems](https://mlsanity.com/paper/1712.09052/) (2017)
- [General-Purpose Visual Language and Information System with Case-Studies in Developing Business Applications](https://mlsanity.com/paper/1712.10281/) (2017)

## Abstract (arXiv)

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