##### Scenario Experian DataLabs Data Scientist technical screen — a machine-learning deep-dive on the modelling choices used in your project, mixed with conceptual questions (some OA / multiple-choice style). ##### Question Walk through the core ML concepts behind a binary-classification project, covering preprocessing, modelling, optimization, and evaluation: 1. Explain how PCA achieves dimensionality reduction and why you would (or would not) apply L2 normalization before training. Distinguish per-column standardization from per-sample (row) L2 normalization, and say when each matters. 2. Derive the logistic-regression gradient via back-propagation, then generalize: describe how backpropagation works in modern multi-layer neural nets. 3. What baseline models did you compare against, and why did you ultimately choose logistic regression? 4. Define knowledge-informed machine learning and give a concrete example. 5. When and how would you move the classification threshold to improve FPR or TPR? Can you improve both FPR and TPR at the same time by moving a single threshold? ##### Hints Discuss eigenvectors/explained variance, maximum-likelihood gradients (prediction error × input), the chain rule through layers, ROC/PR curves and cost-sensitive thresholds, model-selection criteria, and domain priors/constraints.