Browse through our curated collection of machine learning interview questions.
Explain the ROC curve, AUC, and their significance in model evaluation.
23 views
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.
11 views
Explain L1 and L2 regularization techniques and how they differ in terms of their impact on model parameters.
24 views
Explain the different types of gradient descent algorithms and their trade-offs, highlighting their theoretical background and practical applications.
13 views
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?
10 views
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.
14 views
Explain Naive Bayes classification, focusing on its underlying assumptions, different variants, and scenarios where it performs well or poorly.
9 views
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.
22 views
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.