Explain ML basics and recommender tuning
Company: Tubitv
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Explain the following machine learning topics clearly and discuss their practical trade-offs:
- overfitting and common ways to prevent it,
- bagging and when it helps,
- linear regression,
- logistic regression,
- transformer models,
- SGD versus Adam.
Then describe how you would tune model hyperparameters in a real production setting.
Finally, discuss a recommendation system you have worked on or would build in practice: how you would frame the problem, choose features and models, train and evaluate the system, tune it, and handle real-world issues such as cold start, feedback loops, and online experimentation.
Quick Answer: This question evaluates core machine learning competencies including overfitting and regularization, ensemble methods like bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), hyperparameter tuning in production, and end-to-end recommender system design and deployment.