dailyai.report

Cross-entropy measures the average number of bits needed to identify an event from a probability distribution. This mathematical framework links information theory directly to machine learning loss functions. It proves that better prediction equals better compression. Practitioners can use this relationship to evaluate model efficiency beyond standard accuracy benchmarks through a lens of data density.