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This work describes an outlier detection procedure (named "OutlierTree")loosely based on the GritBot software developed by RuleQuest research, whichworks by evaluating and following supervised decision tree splits on variables,in whose branches 1-d confidence intervals are constructed for the targetvariable and potential outliers flagged according to these confidenceintervals. Under this logic, it's possible to produce human-readableexplanations for why a given value of a variable in an observation can beconsidered as outlier, by considering the decision tree branch conditions alongwith general distribution statistics among the non-outlier observations thatfell into the same branch, which can then be contrasted against the value whichlies outside the CI. The supervised splits help to ensure that the generatedconditions are not spurious, but rather related to the target variable andhaving logical breakpoints.

Explainable outlier detection through decision tree conditioning