Logistic regression is a probabilistic classification model. It provides the probability of a particular instance belonging to a class. It is used to predict the probability of binary outcomes. Logistic regression is computationally inexpensive, is relatively easier to implement, and can be interpreted easily.

Logistic regression belongs to the class of discriminative models. The other class of algorithms is generative models. Let's try to understand the differences between the two. Suppose we have some input data represented by X and a target variable Y, the learning task obviously is P(Y|X), finding the conditional probability of Y occurring given X. A generative model concerns itself with learning the joint probability of P(Y, X), whereas a discriminative model will directly learn the conditional probability of P(Y|X) from the training set. This is the actual objective of classification. A generative model first learns P(Y, X), and then gets to P(Y|X) by conditioning...