ML: 1. Linear Regression: 1.1. Explain L1 vs L2? 1.2. How does each affect the coefficients? 1.3. Explain assumptions of linear regression. 1.4. How is each assumption tested? 1.5. If each assumption is violated, what are their remedies? 2. PCA 2.1. Explain PCA. 2.2. Walk me through the algorithm step by step. 2.3. How is the formula constructed? 2.4. What is the relationship between PC1 and PC2? 2.5. How is orthogonality preserved in the mapped feature space? 2.6. How do you run the feature importance in PC-mapped feature space? 3. ML Algorithm 3.1. Explain the ensembling method. 3.2. Explain the differences between XGBoost and Random Forest? 3.3. When is each used? Pros and cons? 3.4. Which one is computationally expensive and why? 3.5. What are the feature selection methodologies? 3.6. Imagine we have a multivariate KPI that most of the features are correlated. Now we are noticing a spike in the KPI, how do you determine which feature has the highest effect on it? (Feature importance analysis for Temporal shock)
