gistfile1.txt

                    \DeclareRobustCommand{\tblcap}[2]{%
                        \caption[\ifstrequal{#2}{cv}{Cross-validation}{Final} learning results (without feature selection)]{Results of regression and classification \ifstrequal{#1}{fs}{after}{without} feature selection, \ifstrequal{#2}{cv}{in cross validation}{on the entire training set and held-out test set}, with summary statistics. \ifstrequal{#2}{cv}{All metrics shown are averaged over cross-validation splits. }For the regression tasks, we calculate the correlation between desired and actual output, slope of the trend line, $p$-value, $R^2$, and \ac{RMSE}, while classification accuracy, $p$-value with respect to a dummy classifier, precision, recall, and \ac{MAE} are shown for the the classification tasks. Bolded rows show the best-performing models. This table corresponds to \cref{fig:properties_#1_#2train,fig:properties_#1_#2test,fig:ratings_#1_#2train,fig:ratings_#1_#2test}.}%
                    }