Classifies a set of test data based on the k Nearest Neighbor algorithm using the training data. The underlying algorithm uses a KD tree and should therefore exhibit reasonable performance. However, this type of classifier is still only suited for a few thousand to ten thousand or so training instances. All (and only) numeric columns and the Euclidean distance are used in this implementation. All other columns (of non-numeric type) in the test data are being forwarded as-is to the output.