Weak Learners
In machine learning, weak learners are models that perform slightly better than random guessing. These models are often used in ensemble learning, where multiple weak learners are combined to create a strong learner. Weak learners are typically simple models, such as decision trees or linear regression models, that are trained on a subset of the data. The goal of using weak learners in ensemble learning is to reduce the risk of overfitting and improve the generalization performance of the model. Weak learners are also used in boosting algorithms, where the weights of misclassified samples are increased to improve the performance of the model on subsequent iterations.
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