  We propose a novel methodology, forest floor, to visualize and interpret
random forest (RF) models. RF is a popular and useful tool for non-linear
multi-variate classification and regression, which yields a good trade-off
between robustness (low variance) and adaptiveness (low bias). Direct
interpretation of a RF model is difficult, as the explicit ensemble model of
hundreds of deep trees is complex. Nonetheless, it is possible to visualize a
RF model fit by its mapping from feature space to prediction space. Hereby the
user is first presented with the overall geometrical shape of the model
structure, and when needed one can zoom in on local details. Dimensional
reduction by projection is used to visualize high dimensional shapes. The
traditional method to visualize RF model structure, partial dependence plots,
achieve this by averaging multiple parallel projections. We suggest to first
use feature contributions, a method to decompose trees by splitting features,
and then subsequently perform projections. The advantages of forest floor over
partial dependence plots is that interactions are not masked by averaging. As a
consequence, it is possible to locate interactions, which are not visualized in
a given projection. Furthermore, we introduce: a goodness-of-visualization
measure, use of colour gradients to identify interactions and an out-of-bag
cross validated variant of feature contributions.
