  Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine
learning models known for achieving strong predictive performance across a wide
variety of domains. However, this strong performance comes at the cost of
interpretability (i.e. users are unable to understand the relationships a
trained random forest has learned and why it is making its predictions). In
particular, it is challenging to understand how the contribution of a
particular feature, or group of features, varies as their value changes. To
address this, we introduce Disentangled Attribution Curves (DAC), a method to
provide interpretations of tree ensemble methods in the form of (multivariate)
feature importance curves. For a given variable, or group of variables, DAC
plots the importance of a variable(s) as their value changes. We validate DAC
on real data by showing that the curves can be used to increase the accuracy of
logistic regression while maintaining interpretability, by including DAC as an
additional feature. In simulation studies, DAC is shown to out-perform
competing methods in the recovery of conditional expectations. Finally, through
a case-study on the bike-sharing dataset, we demonstrate the use of DAC to
uncover novel insights into a dataset.
