Weak Learners

Weak learners are simple models in machine learning that perform slightly better than random guessing. Think of them as basic decision-makers; for example, a weak learner might be a rule that says, "If it’s sunny, then it's likely to be a good day." Alone, these learners aren’t very powerful, but when combined in large numbers—like assembling a team—they can create a strong predictive model. This approach, known as boosting, enhances their accuracy by focusing on the mistakes of previous learners, ultimately resulting in a more effective overall model.