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| -rw-r--r-- | paper/evaluation.tex | 2 |
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diff --git a/paper/evaluation.tex b/paper/evaluation.tex index 3b7f9fb..077b684 100644 --- a/paper/evaluation.tex +++ b/paper/evaluation.tex @@ -35,7 +35,7 @@ The bottom two rows give aggregated results (total and average) over all 44 doma \label{table:eval} \end{table} -Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/ast-patterns/}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method, +Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/aied2017}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method, which had the best overall performance, serve as references for bad and good CA on particular data sets. For example, our rules correctly classified 99\% of testing instances for the \code{sister} problem, |