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Fraud Detection & Anomaly Detection • Unsupervised Anomaly Detection (Isolation Forest, Autoencoders)Medium⏱️ ~2 min

How Does Isolation Forest Work?

Key Insight
Isolation Forest exploits a key property of anomalies: they are easier to isolate. Random splits quickly separate outliers from the bulk of normal data. The fewer splits needed to isolate a point, the more anomalous it is.
The Algorithm
Build a forest of random trees (typically 100-256 trees). For each tree: randomly sample a subset of data, then recursively partition by picking a random feature and random split point within the feature range. Stop when each point is isolated or max depth is reached.
An outlier sitting far from the data cluster gets isolated in 2-3 splits. A normal point deep in a dense cluster requires 10-15 splits. The path length (number of splits to isolate) becomes the anomaly score.
Scoring
Average the path length across all trees. Normalize by the expected path length for a dataset of that size (approximately 2 × (ln(n-1) + 0.5772) - 2(n-1)/n for n samples). Score near 1 means anomaly, near 0.5 means normal, near 0 means the point is in a very dense region.
Why It Works
Random splits are inefficient at separating tightly clustered points but efficient at separating outliers. Outliers have more empty space around them, so any random split has good odds of isolating them. This property makes Isolation Forest robust to the curse of dimensionality that plagues distance-based methods.
💡 Key Advantages: Linear time complexity O(n), no distance calculations (works in high dimensions), handles mixed feature types, minimal hyperparameters (just tree count and subsample size).

💡 Key Takeaways

✓Anomalies are easier to isolate: fewer random splits needed to separate them

✓Build 100-256 random trees, each recursively partitioning with random feature and split point

✓Path length (splits to isolate) becomes anomaly score: short path = more anomalous

✓Score near 1 = anomaly, near 0.5 = normal, near 0 = very dense region

✓Linear O(n) time, no distance calculations, works in high dimensions

📌 Interview Tips

1Explain the intuition: outliers have empty space around them, random splits isolate quickly

2Show path length scoring: 2-3 splits for outlier vs 10-15 for normal point

3Mention advantages: O(n) time, no distance math, handles high dimensions

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