How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Experian.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Experian during technical interviews.

Explain PCA and L2 Normalization in Machine Learning

Quick Overview

An Experian DataLabs Data Scientist technical-screen question that probes core machine-learning foundations: PCA dimensionality reduction and the right kind of normalization, deriving the logistic-regression gradient via backpropagation and generalizing to deep nets, baseline model selection, knowledge-informed ML, and decision-threshold tuning for FPR/TPR. It tests both mathematical fluency and practical model-design judgment, including the subtle point that a single threshold cannot improve TPR and FPR simultaneously without a better score ranking.

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:

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.
Derive the logistic-regression gradient via back-propagation, then generalize: describe how backpropagation works in modern multi-layer neural nets.
What baseline models did you compare against, and why did you ultimately choose logistic regression?
Define knowledge-informed machine learning and give a concrete example.
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.

Quick Overview

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:

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.
Derive the logistic-regression gradient via back-propagation, then generalize: describe how backpropagation works in modern multi-layer neural nets.
What baseline models did you compare against, and why did you ultimately choose logistic regression?
Define knowledge-informed machine learning and give a concrete example.
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?

Explain PCA and L2 Normalization in Machine Learning

Quick Overview

Scenario

Question

Hints

Solution

Submit Your Answer to Earn 20XP

Explain PCA and L2 Normalization in Machine Learning

Quick Overview

Scenario

Question

Hints

Solution

Submit Your Answer to Earn 20XP