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Isolation Forests

Last but not least, isolation forests are an effective method for detecting outliers or novelties in data. It is a relatively novel method based on binary decision trees. Sci-Kit Learn’s implementation is relatively simple and easy to understand.

Isolation forest’s basic principle is that outliers are few and far from the rest of the observations. To build a tree (training), the algorithm randomly picks a feature from the feature space and a random split value ranging between the maximums and minimums. This is made for all the observations in the training set. To build the forest a tree ensemble is made averaging all the trees in the forest.

Then for prediction, it compares an observation against that splitting value in a “node”, that node will have two node children on which another random comparisons will be made. The number of “splittings” made by the algorithm for an instance is named: “path length”. As expected, outliers will have longer path lengths than the rest of the observations.

An outlier score can computed for each observation:

outlier score

https://medium.com/towards-data-science/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561