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https://hackernoon.com/how-much-predictive-signal-is-hidden-in-a-chess-opening.
A technical evaluation of Random Forest vs. MLP neural networks for predicting chess match outcomes using tabular opening data and player ratings.
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We evaluated Random Forest vs. Multi-Layer Perceptron (MLP) models on tabular chess metadata to predict match outcomes. Results show that classical tree-based models outperform deep learning architectures in both accuracy and feature interpretability for structured chess data.