Linear and Boosting Predictors Quiz

Weak learnability in boosting refers to the ability to improve the performance of a weak learner by combining multiple instances of it to create a strong learner. This is achieved by iteratively adjusting the weights of misclassified instances to focus on the hardest examples, ultimately leading to a more accurate prediction model.

In boosting algorithms, linear combinations of base hypotheses are used to create a strong learner by combining the predictions of multiple weak learners. The final prediction is obtained by weighting the individual predictions based on their performance, typically using a weighted majority vote or weighted average.

Logistic regression differs from linear regression in that it is specifically designed for binary classification tasks, where the output is a probability between 0 and 1. This is achieved by using the logistic function to transform the output of the linear predictor, allowing for the modeling of non-linear relationships between the input features and the binary outcome.