Explain metrics, regularization, and ablation studies

Q: Explain metrics, regularization, and ablation studies

This question evaluates understanding of classification evaluation metrics, regularization methods, and experimental design for component-level analysis in NLP and broader Machine Learning systems, testing competencies in performance measurement, regularization trade-offs, and causal attribution of model components.

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Question

You are interviewing for an Applied Scientist role.

For a binary classification problem, explain the following and when you would use each:

Precision, recall, F1
Confusion matrix terms (TP/FP/TN/FN)
ROC curve and AUC
(Optionally) Precision–Recall curve and why it can be preferable under class imbalance

Explain the difference between L1 and L2 regularization:

The mathematical form added to the loss
The effect on learned weights (e.g., sparsity)
Practical guidance on when you would choose L1 vs L2

You have an NLP model with multiple components (e.g., preprocessing, encoder choice, retrieval module, prompt template, reranker, decoding settings). Describe how you would design an ablation study to identify which components materially contribute to performance, including:

What you keep constant vs vary
How you avoid confounders
How you decide whether a change is significant

Explain metrics, regularization, and ablation studies

Quick Overview

Solution

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