\label{tion:lwlOptions}
\input{tables/lwl-fishcatch}
\input{tables/lwl-hungarian}
\input{tables/lwl-bodyfat}
\input{tables/lwl-kc3}
Locally Weighed Naive Bayes, also called Locally Weighted Learning, is a K nearest neighbors algorithm. When using Locally Weighted Learning on the UCI datasets\cite{UCI}, it was noted that the value of K strongly impacted the performance of LWL. I chose to explore $K$ values of 5, 10, 25, 50, and 100. It was also noted that the K value chosen was dataset specific, as shown in \tbl{LWL-fishcatch}, \tbl{LWL-hungarian} and \tbl{LWL-bodyfat}. Note that the optimal $K$ value in the three examples is between 5 and 25. By tuning the $K$ value, historically, the performance of LWL has statistically improved.

When looking at the NASA and SoftLab software defect prediction datasets, I show that across 9 of the 10 datasets\footnote{The exception is KC3}, all values of $K$ performed statistically the same. Although performances look varied, the Mann Whitney Wilcoxin Rank of each learner across the 9 datasets is identical at both the 95\% and 99\% confidence intervals. The results for the 9 identical datasets are shown at the end of this chapter in \tbl{LWL-cm1} - \tbl{LWL-ar5}\label{pg:4-10}. The one outlier, NASA's KC3 dataset is shown in \tbl{LWL-kc3}. This similarity will be looked into further in \chap{results}.
