Explain PCA Dimensionality Reduction and L2-Normalization Importance

Q: Explain PCA Dimensionality Reduction and L2-Normalization Importance

This question evaluates a data scientist's understanding of core Machine Learning concepts including PCA for dimensionality reduction, the impact of L2-normalization on preprocessing, analytic gradient derivation for logistic regression, classification thresholding and its trade-offs, baseline model selection, and knowledge-informed machine learning. It is commonly asked in technical interviews because it probes mathematical foundations and statistical reasoning alongside model-design judgment, testing a blend of conceptual understanding and practical application within the Machine Learning domain.

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Question

Machine-Learning Deep-Dive (Data Scientist Technical Screen)

Scenario

You are discussing core ML concepts and design choices expected in a technical interview setting. Provide concise, principled answers to the following.

Questions

Principal Component Analysis (PCA)

Explain how PCA achieves dimensionality reduction.
Explain why L2-normalization beforehand can matter (clarify what kind of normalization and when).

Logistic Regression

Derive the gradient of the binary logistic regression loss via back-propagation/chain rule.

Classification Thresholding

When would you move the classification threshold to improve FPR or TPR? Describe the trade-offs and how you’d select a new threshold.

Baselines and Model Choice

What baseline models would you compare against, and why might you finally choose logistic regression?

Knowledge-Informed ML

Define knowledge-informed (or knowledge-guided) machine learning and give a concrete example.

Explain PCA Dimensionality Reduction and L2-Normalization Importance

Machine-Learning Deep-Dive (Data Scientist Technical Screen)

Scenario

Questions

Solution

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