Identify Fake Accounts Using Machine Learning Techniques

Q: Identify Fake Accounts Using Machine Learning Techniques

This question evaluates competencies in applied machine learning for detecting fake accounts on social networks, covering feature engineering, handling class imbalance and noisy labels, model selection, evaluation metrics, and production deployment considerations.

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Question

Context

You are a data scientist at a large social platform. The goal is to detect and mitigate fake or abusive accounts while minimizing harm to legitimate users. Fake accounts are rare compared to legitimate ones, so class imbalance, noisy labels, and high business costs of mistakes are central concerns.

Tasks

End-to-end approach: Describe how you would design an end-to-end system to identify fake accounts.
Data and labels: Explain how you would obtain training labels and address label noise and class imbalance.
Features: Propose key feature families and give examples for each.
Modeling and training: Outline model choices, training setup, handling imbalance, and how you’d prevent leakage and drift.
Evaluation and prioritization: Specify offline and online evaluation metrics, how you would set thresholds, and how you would prioritize precision vs. recall given business costs.

Identify Fake Accounts Using Machine Learning Techniques

Context

Tasks

Solution

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Identify Fake Accounts Using Machine Learning Techniques

Overview

Detecting Fake Accounts on a Social Network

Context

Tasks

Solution

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