MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model explains how the data was generated, while a discriminative model focuses on predicting the labels of the data.

Detailed explanation-2: -The generative approach focuses on modeling, whereas the discriminative approach focuses on a solution. So, we can use generative algorithms to generate new data points. Discriminative algorithms don’t serve that purpose. Still, discriminative algorithms generally perform better for classification tasks.

Detailed explanation-3: -A discriminative model focuses on predicting the labels of the data, whereas a generative model focuses on describing how the data was formed. This is another significant distinction between the two types of models.

Detailed explanation-4: -Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick.

Detailed explanation-5: -Generative and discriminative models are widely used machine learning models. For example, Logistic Regression, Support Vector Machine and Conditional Random Fields are popular discriminative models; Naive Bayes, Bayesian Networks and Hidden Markov models are commonly used generative models.