  Many machine learning models have important structural tuning parameters that
cannot be directly estimated from the data. The common tactic for setting these
parameters is to use resampling methods, such as cross--validation or the
bootstrap, to evaluate a candidate set of values and choose the best based on
some pre--defined criterion. Unfortunately, this process can be time consuming.
However, the model tuning process can be streamlined by adaptively resampling
candidate values so that settings that are clearly sub-optimal can be
discarded. The notion of futility analysis is introduced in this context. An
example is shown that illustrates how adaptive resampling can be used to reduce
training time. Simulation studies are used to understand how the potential
speed--up is affected by parallel processing techniques.
