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 in 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). Full procedure is described in Cortes (2020)