Chess Puzzle Classifier
Using machine learning to predict puzzle categories
By: Barak Finnegan, Tanvi Haldankar, James Park
Project Abstract
This work compares four different machine learning approaches in classifying chess puzzles. We trained Support Vector Machine (SVM), Deep Feed Forward Neural Network (DFFNN), Convolutional Neural Networks (CNN), and ensemble models and compared how they performed on classifying tasks. The Deep Feed Forward Neural Network performed best out of the three approaches, while the Support Vector Machine and Convolutional Neural Network performed similarly but worse. The ensemble model had a higher true accuracy but predicted a single theme worse than other models. In the future, this work could be expanded to take in a full game from any user and determine where puzzle positions arose.
Final Paper
Chess_Puzzles.pdfDemonstration Video
Our team member, Tanvi, showcases our project!
Support Vector Machine
A Support Vector Machine is good at generalization and avoids overfitting.
Deep Feed Forward NN
A Deep Feed Forward Neural Network allows us to approximate a classifier while making no assumptions about our input data.
Convolutional NN
A Convolutional Neural Network allows us to take spatial structure of input data into account.
Data Analysis
Analysis heat maps of the kings for different positions, and the distribution of puzzles to gain insight on the makings of chess puzzles.
Final Results
The performance of each respective model.
Deep Feed Forward NN
This model accurately predicted its highest probability class over 94% of the time!
Support Vector Machine
This model performed at a 73% accuracy.
Convolutional NN
This model accurately predicted its highest probability class 76.4% of the time.