👉 Basket math is a method used in machine learning to handle multi-class classification problems by transforming them into multiple binary classification tasks. Instead of directly predicting a single class label, the goal is to predict the presence or absence of each class within a "basket" or subset of features. Each basket represents a combination of features, and the model learns to determine which combinations are indicative of each class. This approach simplifies the problem, making it easier to apply binary classification techniques and often improves model performance by leveraging the interdependencies between features. By breaking down the problem into these smaller, manageable tasks, basket math enables more efficient training and better generalization on unseen data.
