  Recent studies have shown that tuning prediction models increases prediction
accuracy and that Random Forest can be used to construct prediction intervals.
However, to our best knowledge, no study has investigated the need to, and the
manner in which one can, tune Random Forest for optimizing prediction intervals
{ this paper aims to fill this gap. We explore a tuning approach that combines
an effectively exhaustive search with a validation technique on a single Random
Forest parameter. This paper investigates which, out of eight validation
techniques, are beneficial for tuning, i.e., which automatically choose a
Random Forest configuration constructing prediction intervals that are reliable
and with a smaller width than the default configuration. Additionally, we
present and validate three meta-validation techniques to determine which are
beneficial, i.e., those which automatically chose a beneficial validation
technique. This study uses data from our industrial partner (Keymind Inc.) and
the Tukutuku Research Project, related to post-release defect prediction and
Web application effort estimation, respectively. Results from our study
indicate that: i) the default configuration is frequently unreliable, ii) most
of the validation techniques, including previously successfully adopted ones
such as 50/50 holdout and bootstrap, are counterproductive in most of the
cases, and iii) the 75/25 holdout meta-validation technique is always
beneficial; i.e., it avoids the likely counterproductive effects of validation
techniques.
