When to Use Discriminative?
In Simple Words, a Discriminative Model Makes Predictions on the Unseen Data Based on Conditional Probability and Can Be Used Either for Classification or...
In simple words, a discriminative model makes predictions on the unseen data based on conditional probability and can be used either for classification or regression problem statements. On the contrary, a generative model focuses on the distribution of a dataset to return a probability for a given example.
What is the difference between discriminative and generative?
In simple words, a discriminative model makes predictions based on conditional probability and is either used for classification or regression. On the other hand, a generative model revolves around the distribution of a dataset to return a probability for a given example.
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What are the advantages of discriminative classifier?
It allows a discriminative model to better learn the interactions between classes and their relative distributions for discrimination. Thus, as long as the discriminative model is not too computationally intensive and the volume of data is tractable, training on all the data is not a problem.
David Miller brings 15 years of experience in global economics, personal finance strategy, and market dynamics. He specializes in turning complex economic trends into actionable insights for everyday readers.
