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Classification Techniques for Board Games by Wiktor Skindel is a document available to read on EtoBox.

What is Classification Techniques for Board Games about?

The document evaluates and compares various classification techniques for predicting outcomes in strategic board games using three datasets: Tic-Tac-Toe, Chess, and Connect4. It employs WEKA software to analyze the performance of classifiers such as Naïve Bayes, MLP, SMO, K-NN, LMT, and Random Forest based on metrics like accuracy and recall. The study concludes that tree-based LMT, SVM-based SMO, and K-NN-based LBk are the most effective models for outcome prediction, with LMT being the most influential.

Author

Wiktor Skindel

Language

EN