Discriminative Models are a type of machine learning models that learn to distinguish between different kinds of data.
Let’s say you have a basket of fruits, and your job is to separate the apples from the oranges. You would look at each fruit and decide whether it’s an apple or an orange based on its features – like its color, shape, and size. That’s sort of what a discriminative model does.
A discriminative model is trained on a lot of data, and it learns to tell the difference between different categories or classes of data. For example, it might be trained on pictures of different fruits, and learn to tell the difference between apples, oranges, bananas, and so on. It does this by learning the features that distinguish each class from the others.
Discriminative models are used in many areas of artificial intelligence, like image recognition, where they can identify what’s in a picture, or speech recognition, where they can understand what someone is saying.
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