ML Interview Questions

Explain the concept of Support Vector Machines (SVM) in detail. Describe how SVMs perform classification, including the role of hyperplanes and support vectors. Discuss the importance of the kernel trick, and provide examples of different kernels that can be used. How do these kernels impact the decision boundaries?

Explain gradient boosting algorithms. How do they work, and what are the differences between XGBoost, LightGBM, and CatBoost?

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?

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?