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 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 the differences between linear regression and logistic regression, focusing on their objectives, assumptions, and the types of problems they are best suited to solve.

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.

Explain how decision trees work, including the algorithm's approach to splitting nodes and handling both categorical and continuous variables.