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Explain PCA and L2 Normalization in Machine Learning

ExperianData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain PCA and L2 Normalization in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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.