ML Interview Questions

Can you explain the bias-variance tradeoff in machine learning? How does this tradeoff influence your choice of model complexity and its subsequent performance on unseen data?

Provide a comprehensive explanation of ensemble learning methods in machine learning. Compare and contrast bagging, boosting, stacking, and voting techniques. Explain the mathematical foundations, advantages, limitations, and real-world applications of each approach. When would you choose one ensemble method over another?

Can you describe how decision trees use information gain to decide which feature to split on at each node? How does this process contribute to creating an efficient and accurate decision tree model?

Imagine you are working on a binary classification task and your dataset is highly imbalanced. Explain how you would approach evaluating your model's performance. Discuss the limitations of accuracy in this scenario and which metrics might offer more insight into your model's performance.

Describe and compare different techniques for anomaly detection in machine learning, focusing on statistical methods, distance-based methods, density-based methods, and isolation-based methods. What are the strengths and weaknesses of each method, and in what situations would each be most appropriate?