weak classifiers

A weak classifier is a machine learning model that makes predictions only slightly better than random guessing. While it may not be very accurate on its own, it can still identify patterns to some degree. When many such weak classifiers are combined—using techniques like boosting—they create a stronger, more reliable overall system. Think of each weak classifier as a small clue; together, they form a clearer picture. This approach allows complex problems to be tackled effectively by iterative improvement, even if individual components are only modestly accurate.