How Much Predictive Signal Is Hidden in a Chess Opening?

This story was originally published on HackerNoon at: 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. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #deep-learning, #random-forest, #chess-machine-learning, #multi-layer-perceptron, #feature-perception, #one-hot-encoding, #tabular-machine-learning, #hackernoon-top-story, and more. This story was written by: @oteope. Learn more about this writer by checking @oteope's about page, and for more stories, please visit hackernoon.com. 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.