Beta. Content is under active construction and has not been peer-reviewed. Report errors on
GitHub
.
Disclaimer
Theorem
Path
Curriculum
Paths
Demos
Diagnostic
Search
Quiz Hub
/
Anomaly Detection
Anomaly Detection
3 questions
Difficulty 3-4
View topic
Foundation
0 / 3
2 foundation
1 intermediate
Adapts to your performance
1 / 3
foundation (3/10)
compare
Anomaly detection identifies rare patterns that differ from the majority of data. Which approach treats anomalies as low-probability points under a learned density?
Hide and think first
A.
Reconstruction-based: train a regression model to predict one feature from others, flag points with high residual
B.
Density-based methods: fit a probability model to the normal data, flag points with density below a threshold as anomalies
C.
Clustering: run k-means, flag points far from any centroid
D.
Isolation-based: random trees; anomalies are 'easier to isolate' by random splits
Submit Answer