Isolation Forest

An isolation tree repeatedly chooses a feature and a split value, partitioning observations until they are isolated or a limit is reached. Observations isolated through shorter paths are assigned stronger anomaly evidence, and a forest aggregates this behavior across trees. The approach relies on the idea that unusual points can be easier to separate under random partitioning. Sampling, feature selection and tree settings affect the score. A threshold converts that score to an outlier label and may be based on an assumed contamination level. The score should be interpreted as the model's isolation-based deviation measure rather than a calibrated probability of a particular incident.

Define which observations should form the reference and prepare features with attention to semantics and irrelevant dimensions. Fit the detector on a suitable historical window, inspect score distributions and calibrate thresholds using reviewed cases or realistic investigation capacity. Compare alerts across seeds and operating conditions, and examine representative high-score normal cases. The result includes scoring and threshold logic, a reviewer feedback process and monitoring for drift, making clear how model deviation becomes an operational alert and where human interpretation remains necessary.

Unusualness depends on the reference population, and rare normal subgroups can be flagged repeatedly. Irrelevant features or changing activity patterns can weaken scores. A contamination setting determines a thresholding assumption rather than discovering the true incident rate. Standard scores are not calibrated event probabilities. Isolation Forest differs from random-forest classification because it does not use class labels to learn a boundary. Assess alert burden and detection behavior against relevant cases instead of judging the method by how many observations it labels anomalous.