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In a supervised learning model, a labelled dataset is given to the machine. A labelled dataset is the information
              which is tagged with identifiers of data. For example, clothes in a store are marked under various categories of
              clothing like Shirts, Trousers, Coats, etc. They are further labelled as per gender and size.

              Discriminative Modelling

              Discriminative modelling is an approach in machine learning where the focus is on learning the boundary or
              decision boundary that separates different classes or categories directly from the data. So, if an image contains
              a combination of Dogs and Cats, the model is able to tell which is a Dog and which is a Cat.
              Input Image

                                   Label: Dogs                                         Label: Cats











              Output













                                           Cats = 2                            Dog = 1

              In supervised learning, discriminative modelling contrasts with generative modelling, where the goal is to model
              the joint probability distribution of both the input features and the output labels. Generative models can be
              used to generate new data points that resemble the training data, whereas discriminative models are primarily
              focused on classification or regression tasks.
              Let us consider an example.

              A father has two kids, Kid A and Kid B. Kid A has a special learning ability where he can learn everything in depth.
              Kid B has a special learning ability where he can only learn the differences between what he saw.

              One fine day, the father shows his kids (Kid A and Kid B) two kinds of animals, a dog and a cat. After a few days,
              the father showed them an animal and asked both of them “Is this animal a dog or a cat?”
              Kid A drew the image of a dog and a cat on a piece of paper based on what he saw earlier. He compared both the
              images with the animal standing before him and answered based on the closest match of the image and animal,
              he answered: “The animal is a dog.” Kid B knows only the differences, based on different properties learned, he
              answered: “The animal is a dog.”

              Here, we can see both of them are finding the kind of animal, but the way of learning and the way of finding
              the answer is entirely different. In machine learning, we generally call Kid A as a generative model & Kid B as a
              discriminative model.

              A discriminative model models the decision boundary between the classes. A generative model explicitly models
              the actual distribution of each class. In final both of them is predicting the conditional probability P (Animal
              Features). But both models learn different probabilities.


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