Explainable outlier detection through decision tree conditioning
- URL: http://arxiv.org/abs/2001.00636v1
- Date: Thu, 2 Jan 2020 21:45:52 GMT
- Title: Explainable outlier detection through decision tree conditioning
- Abstract summary: GritBot software works by evaluating and following supervised decision tree splits on variables.
It's possible to produce human-readable explanations for why a given value of a variable in an observation can be considered as outlier.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work describes an outlier detection procedure (named "OutlierTree")
loosely based on the GritBot software developed by RuleQuest research, which
works by evaluating and following supervised decision tree splits on variables,
in whose branches 1-d confidence intervals are constructed for the target
variable and potential outliers flagged according to these confidence
intervals. Under this logic, it's possible to produce human-readable
explanations for why a given value of a variable in an observation can be
considered as outlier, by considering the decision tree branch conditions along
with general distribution statistics among the non-outlier observations that
fell into the same branch, which can then be contrasted against the value which
lies outside the CI. The supervised splits help to ensure that the generated
conditions are not spurious, but rather related to the target variable and
having logical breakpoints.
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