Explainable Outlier Detection Through Decision Tree Conditioning

Will try to fit decision trees that try to "predict" values for each column based on the values of each other column. Along the way, each time a split is evaluated, it will take the observations that fall into each branch as a homogeneous cluster in which it will search for outliers in the 1-d distribution of the column being predicted. Outliers are determined according to confidence intervals on this 1-d distribution, and need to have a large gap with respect to the next observation in sorted order to be flagged as outliers. Since outliers are searched for in a decision tree branch, it will know the conditions that make it a rare observation compared to others that meet the same conditions, and the conditions will always be correlated with the target variable (as it's being predicted from them). Loosely based on the 'GritBot' < https://www.rulequest.com/gritbot-info.html> software.


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install.packages("outliertree")

1.0.4 by David Cortes, 3 months ago


https://github.com/david-cortes/outliertree


Report a bug at https://github.com/david-cortes/outliertree/issues


Browse source code at https://github.com/cran/outliertree


Authors: David Cortes


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp

Linking to Rcpp, Rcereal


See at CRAN