Weak Learning

Weak learning refers to a process where a machine learning algorithm can make predictions slightly better than random guessing, even if not highly accurate. It’s like having a student who occasionally guesses correctly more often than chance, but not confidently. The idea is that, by combining many such weak learners, you can create a strong, reliable model. This concept forms the basis for ensemble methods like boosting, which improve overall performance by integrating multiple modest predictors into a powerful one. Essentially, weak learning is about making incremental improvements that, when combined, lead to impressive results.