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This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Spokeo.

What role is this question designed for?

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

Explain Key Terms in Model Evaluation for Fraud Detection

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of classification model-evaluation metrics (precision, recall/sensitivity, specificity, F1-score) and basic statistical inference (p-value), along with the competency to map metric trade-offs to business cost and user-experience implications in a fraud-detection scenario.

Spokeo

Aug 4, 2025, 10:55 AM

Data Scientist

Onsite

Machine Learning

Model Evaluation Terminology and Business Trade-offs

Scenario

Phone screen focused on understanding core model-evaluation metrics and their business implications.

Tasks

Define the following classification metrics and provide their formulas:
- Precision
- Recall (Sensitivity)
- Specificity
- F1-score
Explain what a p-value represents in hypothesis testing.
In a fraud-detection scenario, argue whether the false positive rate (FPR) or false negative rate (FNR) is more critical, and justify in terms of business cost and user experience.

Note: Tie each metric to business cost and clearly explain trade-offs.

Solution

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