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 ...

Start Free Trial

No credit card required