  The isolation forest algorithm for outlier detection exploits a simple yet
effective observation: if taking some multivariate data and making uniformly
random cuts across the feature space recursively, it will take fewer such
random cuts for an outlier to be left alone in a given subspace as compared to
regular observations. The original idea proposed an outlier score based on the
tree depth (number of random cuts) required for isolation, but experiments here
show that using information about the size of the feature space taken and the
number of points assigned to it can result in improved results in many
situations without any modification to the tree structure, especially in the
presence of categorical features.
