What is the difference between a generative and discriminative model?
Theme: Machine Learning Concepts Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Machine Learning Concepts with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Definition: Generative models are used to model the joint probability distribution of the input features and the target labels, while discriminative models are used to model the conditional probability distribution of the target labels given the input features
- Training: Generative models are trained by maximizing the likelihood of the observed data, while discriminative models are trained by minimizing a loss function that measures the discrepancy between the predicted labels and the true labels
- Data Usage: Generative models can be used for both classification and generation tasks, as they can generate new samples from the learned distribution. Discriminative models are primarily used for classification tasks, as they focus on learning the decision boundary between different classes
- Feature Representation: Generative models explicitly model the joint distribution of the input features and the target labels, which can capture dependencies between them. Discriminative models only model the conditional distribution of the target labels given the input features, which may ignore some of the dependencies
- Data Efficiency: Generative models tend to be more data-efficient as they learn the underlying distribution of the data. Discriminative models can be more effective when the amount of labeled data is limited, as they focus on learning the decision boundary
- Performance: Generative models can suffer from the curse of dimensionality when dealing with high-dimensional data. Discriminative models can achieve better performance in classification tasks when the decision boundary is complex and the classes are well-separated
- Examples: Examples of generative models include Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), and Variational Autoencoders (VAE). Examples of discriminative models include Logistic Regression, Support Vector Machines (SVM), and Neural Networks (NN)
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Knowledge of machine learning concepts: Understanding the difference between generative and discriminative models is a fundamental concept in machine learning
- Ability to apply appropriate models: The interviewer wants to assess if you can identify when to use generative or discriminative models based on the problem at hand
- Critical thinking skills: By asking this question, the interviewer wants to evaluate your ability to analyze and compare different approaches in machine learning
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Providing incorrect or vague definitions of generative and discriminative models
- Confusion: Mixing up the concepts or using incorrect terminology while explaining the differences
- Incomplete answer: Failing to mention key differences such as the focus on probability distribution in generative models and decision boundary in discriminative models
- Lack of examples: Not providing clear examples or use cases to illustrate the practical differences between the two types of models
- Overgeneralization: Making sweeping statements or generalizations about generative and discriminative models without acknowledging exceptions or nuances