Data Science Interview Questions - Sample

Q. Explain the difference between Type I error & Type II error.

Q. How will you avoid overfitting and underfitting and hence build a robust model?

Q. How is Random Forest different from GBM, both being tree based?

Q. How do you select important variables in your model?

Q. How do you handle imbalanced datasets? Which is more important to reduce, False Positives or True Negatives?

Q. Explain the purpose of Cross Validation and how it is done.

Q. How do you tune parameters in Random Forest & XGBoost?

Q. When do we use Ridge & Lasso Regression?

Q. How do you measure accuracy in a Multi-class Classification problem?

Q. What are the best practices for handling missing values in a dataset?

Q. When does regularization becomes necessary in Machine Learning?

Q. What do you understand by Bias Variance trade off?

Q. Explain the purpose of using OLS in linear regression & Maximum likelihood in logistic regression.