\input{fig/results}
%\input{fig/results-with-rank}
\input{fig/wlt-usualSuspects}
\input{fig/wlt-nbridorclump}
\fig{results-WC} - \fig{results-CC-Log} show the results in quartile
form. In both Within-Company and Cross-Company, when the
numbers are not logged, Clump has a higher probability of detection(PD)
than any of the other learners. At the same time, it also has the
highest probability of failure{PF}. When the numerics are logged, all
of the learners except Naive Bayes and Clump remain the same while Clump
and Naive Bayes increase their PD. There are three things of note: 
\bi
\item In all of the experiments, The only learner that performs close to, or better than, Clump is Naive Bayes. The PD's of other learners are between 22\% and 52\% lower than Clump with PF's that are only between 
\item Clump shows a high increase in PD when working on Cross-Company
      data, with a proportionally lower increase in PF.
\item Taking the logarithm of the data increases the PD for Naive Bayes
      and Clump. The PF of Naive Bayes also increases, while the PF of
      Clump stays the same.
\ei
