  Machine learning models fit complex algorithms to arbitrarily large datasets.
These algorithms are well-known to be high on performance and low on
interpretability. We use interactive visualization of slices of predictor space
to address the interpretability deficit; in effect opening up the black-box of
machine learning algorithms, for the purpose of interrogating, explaining,
validating and comparing model fits. Slices are specified directly through
interaction, or using various touring algorithms designed to visit
high-occupancy sections or regions where the model fits have interesting
properties. The methods presented here are implemented in the R package
\pkg{condvis2}.
