  Random forests are among the most popular classification and regression
methods used in industrial applications. To be effective, the parameters of
random forests must be carefully tuned. This is usually done by choosing values
that minimize the prediction error on a held out dataset. We argue that error
reduction is only one of several metrics that must be considered when
optimizing random forest parameters for commercial applications. We propose a
novel metric that captures the stability of random forests predictions, which
we argue is key for scenarios that require successive predictions. We motivate
the need for multi-criteria optimization by showing that in practical
applications, simply choosing the parameters that lead to the lowest error can
introduce unnecessary costs and produce predictions that are not stable across
independent runs. To optimize this multi-criteria trade-off, we present a new
framework that efficiently finds a principled balance between these three
considerations using Bayesian optimisation. The pitfalls of optimising forest
parameters purely for error reduction are demonstrated using two publicly
available real world datasets. We show that our framework leads to parameter
settings that are markedly different from the values discovered by error
reduction metrics.
