Rhythm-game Difficulty Classification with Machine Learning

Question

Which measurable patterns in a rhythm-game chart determine its difficulty?

Goals

• Extract mathematical features from rhythm-game charts.
• Train and evaluate a model that predicts difficulty.

Method

Turn note timing and movement patterns into data that a classifier can compare.

• Measure features such as note density, interval variation, jumps, and repeated patterns.
• Pair charts with difficulty labels and split them into training and test sets.
• Train a classifier and inspect both accuracy and the charts it misclassifies.