General ML theory and practice questions during a technical interview.

a) How do you handle missing values before model training and why?

b) Given a business scenario, how would you choose an appropriate ML algorithm and justify it?

c) Explain Random Forests in lay terms and contrast them with linear regression.

d) Define overfitting vs. underfitting and methods to detect/mitigate each.

Cover imputation, algorithm bias-variance trade-off, ensemble intuition, cross-validation and regularization.

Amazon Data Scientist interview question: Handle Missing Values and Choose ML Algorithms Wisely. {"blocks": [{"key": "6ea8d94b", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...