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 Meta.

What role is this question designed for?

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

Detect and Reduce Spammy Friend Requests Effectively

Quick Overview

This question evaluates a data scientist's applied machine learning competencies for abuse detection on social platforms, covering feature engineering, label generation strategies, handling class imbalance, model calibration, interpretability, and product integration.

Detecting Spammy Friend Requests

Context

Assume a consumer social platform where users can send friend requests (optionally with a short message). The goal is to protect user experience by reducing spammy requests while preserving legitimate connections, especially for new users.

Tasks

Definition
- Clearly define what constitutes a "spammy" friend request on this platform.
Feature Engineering
- List the key features/signals you would engineer to identify spammy requests (sender, recipient, pairwise, graph, content, device, temporal, and feedback signals).
Modeling Approach
- Describe how you would build an initial classification model, including data splitting, handling class imbalance, model choice, interpretability, and calibration.
Labels When None Exist
- If no labeled data exist, explain strategies to obtain/generate labels (heuristics, human-in-the-loop, semi-/weak supervision, and positive–unlabeled learning).
Product Integration
- Once live, how would you use the model to improve user experience (interventions and experimentation)?
Precision–Recall Trade-off
- How would you determine, set, and monitor the appropriate precision–recall trade-off for this problem?

Quick Overview

Tasks

Definition

Clearly define what constitutes a "spammy" friend request on this platform.

Feature Engineering

List the key features/signals you would engineer to identify spammy requests (sender, recipient, pairwise, graph, content, device, temporal, and feedback signals).

Modeling Approach

Describe how you would build an initial classification model, including data splitting, handling class imbalance, model choice, interpretability, and calibration.

Labels When None Exist

If no labeled data exist, explain strategies to obtain/generate labels (heuristics, human-in-the-loop, semi-/weak supervision, and positive–unlabeled learning).

Product Integration

Once live, how would you use the model to improve user experience (interventions and experimentation)?

Precision–Recall Trade-off

How would you determine, set, and monitor the appropriate precision–recall trade-off for this problem?

Detect and Reduce Spammy Friend Requests Effectively

Quick Overview

Detecting Spammy Friend Requests

Context

Tasks

Solution

Submit Your Answer to Earn 20XP

Detect and Reduce Spammy Friend Requests Effectively

Quick Overview

Detecting Spammy Friend Requests

Context

Tasks

Solution

Submit Your Answer to Earn 20XP