👉 Clicks math is a fundamental concept in information theory and machine learning, particularly in the context of natural language processing (NLP) and text classification. It quantifies the amount of information conveyed by a single word or token in a given context, typically measured using the entropy and mutual information. The basic idea is that each unique token (word or character) contributes a certain amount of information, which is inversely proportional to its probability of occurrence. The entropy of a token set gives the average amount of information produced by that set, while mutual information measures how much knowing one token reduces uncertainty about another. In practice, clicks math is used to evaluate the effectiveness of different features or models by comparing the information gain from using those features or models in predictions versus a baseline (often random guessing). This helps in feature selection and model optimization, ensuring that the most informative tokens are prioritized.
