Isolation Forest — how does it detect anomalies?

medium

Answer

$Build many random trees splitting on random features and thresholds.$
$Anomalies are 'isolated' with fewer splits than inliers (shorter path length).$
$Score s(x, n) = 2^(-E[h(x)] / c(n)) where E[h] = average path length, c(n) = normalization.$
$Fast O(n log n), works well in high dims, no density estimation.$
$Standard first choice for tabular anomaly detection.$

Check yourself — multiple choice

Random
Random trees; anomalies isolated with shorter paths; score = 2^(-E[h]/c(n)); fast O(n log n) high-dim tabular default
Same as k-means
Not real

Isolation Forest: shorter random-tree paths for anomalies; standard tabular AD.

#anomaly-detection

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