Generative vs discriminative models in Prompt Engineering / GenAI - Quick Revision & Key Differences

Recall & Review

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What is a generative model in machine learning?

A generative model learns how data is created by modeling the joint probability of inputs and outputs. It can generate new data similar to the training data.

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What is a discriminative model in machine learning?

A discriminative model learns the boundary between classes by modeling the conditional probability of outputs given inputs. It predicts labels for new data.

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Give a real-life example of a generative model.

A generative model is like a chef who learns recipes and can create new dishes. For example, a model that creates new images of faces after learning from many photos.

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Give a real-life example of a discriminative model.

A discriminative model is like a security guard who checks if someone is allowed in or not. For example, a spam filter that decides if an email is spam or not.

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What is the main difference between generative and discriminative models?

Generative models learn how data is made and can create new data. Discriminative models learn to tell classes apart and predict labels.

Which model type can generate new data similar to the training set?

A Both

B Discriminative model

C Neither

D Generative model

Generative models learn the data distribution and can create new samples similar to training data.

Which model focuses on predicting the label given the input?

A Generative model

B Unsupervised model

C Discriminative model

D Reinforcement model

Discriminative models learn the boundary between classes and predict labels from inputs.

Which of these is an example of a discriminative model?

A Logistic Regression

B GAN (Generative Adversarial Network)

C Naive Bayes

D Variational Autoencoder

Logistic Regression is a discriminative model that predicts class labels.

Which model type models the joint probability of inputs and outputs?

A Generative model

B Clustering model

C Regression model

D Discriminative model

Generative models model the joint probability P(inputs, outputs).

What is a key advantage of generative models?

A They are faster to train

B They can create new data samples

C They always have higher accuracy

D They require less data

Generative models can create new data samples similar to the training data.

Explain in your own words the difference between generative and discriminative models.

Think about whether the model creates data or just classifies it.

Give an example of a real-life situation where you would use a generative model and one where you would use a discriminative model.

Consider if the task needs new data or just classification.