Given some data points, the discriminative model learns to classify the data points into their respective classes by learning the decision boundary that separates the classes in an optimal way. The generative models can also classify given data points, but instead of learning the decision boundary, they learn the characteristics of each of the classes.

For instance, let's consider the image classification task for predicting whether a given image is an apple or an orange. As shown in the following figure, to classify between apple and orange, the discriminative model learns the optimal decision boundary that separates the apples and oranges classes, while generative models learn their distribution by learning the characteristics of the apple and orange classes:

To put it simply, discriminative models learn to find...