Normal samples require longer isolation steps, while anomalous ones have on average a shorter tree length. Yet, the isolation forest algorithm is a non-deterministic method. Each run of the algorithm on the same data set might result in a slightly different classification of the molecules very close to the border region of in-and outliers as the split is created by randomly branching the tree. Figure illustrates the difference in the required tree length for the isolation of a normal, xi, and an anomalous, xx, data point. The algorithm is used as implemented in the python module Scikit-learn. For details on parameterisation see ESI.