Explainable Outlier Detection Through Decision Tree Conditioning
Convert outlier outputs to R list
Check values that could potentially flag an observation as outlier
Extract outliers found in training data
Outlier Tree
Predict method for Outlier Tree
Print outliers in human-readable format
Print summary information from Outlier Tree model
Slice or sub-set outliers
Print outliers in human-readable format
Print summary information from Outlier Tree model
Get Variable Names for OutlierTree Model
Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
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