Both discriminative and generative models have the ability to learn model parameters in a classification setting, but they are based on entirely different mechanisms and serve different purposes.
Linear models are a class of models in which a response variable is linearly related to one or more predictors.
Logistic regression is the most traditional classification algorithm and preserves many of the advantages in interpretation as linear regression for a continuous outcome.
A weak learner refers to a prediction mechanism that produces results that are only slightly more predictive than those resulting from a random chance model.
If the overall distribution of the outcome is heavily tilted towards one class compared to the other, the classification problem is considered to be imbalanced.
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