ML Interview Questions

Explain the ROC curve, AUC, and their significance in model evaluation.

Explain the differences between bagging and boosting in ensemble learning. Provide examples of algorithms that use each technique and discuss their respective advantages and potential drawbacks in terms of model performance and computational complexity.

Explain L1 and L2 regularization techniques and how they differ in terms of their impact on model parameters.

Explain the Random Forest algorithm. How does it improve upon decision trees? Discuss the process of creating a random forest, including the role of bootstrapping and feature randomness. What are some practical applications of this algorithm, and how would you implement it in a real-world scenario?

Explain Principal Component Analysis (PCA) and how it can be used for dimensionality reduction. Discuss its underlying mathematical principles, practical applications, and any potential limitations or drawbacks. Illustrate your explanation with examples or diagrams where possible.

Explain Naive Bayes classification, focusing on its underlying assumptions, different variants, and scenarios where it performs well or poorly.

Explain L1 (Lasso) and L2 (Ridge) regularization in the context of linear models. Discuss their mathematical formulations, the differences in their effects on model parameters, and scenarios where one might be preferred over the other.

Can you explain the working mechanism of the K-Nearest Neighbors (KNN) algorithm for both classification and regression tasks? Discuss its strengths and limitations. How do you determine the optimal value of K? Additionally, elaborate on the concept of the curse of dimensionality in relation to KNN.

Explain how the random forest algorithm works and why it is often more effective than a single decision tree. Include the concepts of bagging and feature randomness in your explanation.

Explain the process of k-fold cross-validation and its significance in evaluating machine learning models.