gistfile1.txt

                    \DeclareRobustCommand{\tblcap}[2]{%
                        \caption[\ifthenelse{\equal{#2}{cv}}{Cross-validation}{Final} learning results (without feature selection)]{Results of regression and classification \ifthenelse{\equal{#1}{fs}}{after}{without} feature selection, \ifthenelse{\equal{#2}{cv}}{in cross validation}{on the entire training set and held-out test set}, with summary statistics. \ifthenelse{\equal{#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}.}%
                    }

                    \begin{table}[p]
                        \centering
                        \begin{tabular}{r|>{\rowmac}c >{\rowmac}c >{\rowmac}c >{\rowmac}c >{\rowmac}c | >{\rowmac}c >{\rowmac}c >{\rowmac}c >{\rowmac}c >{\rowmac}c <{\clearrow}}
                            \multicolumn{1}{l}{\textbf{NOFS-CV}} \\
                            \multicolumn{1}{l}{} \\
                            \multirow{2}{*}{Property} & \multicolumn{5}{c|}{CV Training set performance} & \multicolumn{5}{c}{CV Test set performance} \\
                                                      & $\rho$ & $m$  & $p$          & $R^2$ & RMSE & $\rho$ & $m$   & $p$            & $R^2$  & RMSE \\ \hline
%\setrow{\bfseries\boldmath}
                            Frequency bin 1       & 0.97 & 0.87 & $<10^{-100}$ & 0.94 & 0.24 & 0.27 & 0.091 & $1.1*10^{-6}$  & 0.049   & 0.95 \\
                            Frequency bin 10      & 0.91 & 0.59 & $<10^{-100}$ & 0.75 & 0.5  & 0.16 & 0.035 & 0.091          & -0.036  & 0.97 \\
\setrow{\bfseries\boldmath} Friction coefficient  & 0.99 & 0.95 & $<10^{-100}$ & 0.98 & 0.14 & 0.31 & 0.13  & $9.1*10^{-12}$ & 0.058   & 0.96 \\
                            Hardness              & 0.99 & 0.93 & $<10^{-100}$ & 0.97 & 0.17 & 0.28 & 0.11  & 0.0002         & 0.068   & 0.96 \\
                            Spectral centroid     & 0.99 & 0.92 & $<10^{-100}$ & 0.97 & 0.17 & 0.18 & 0.051 & 0.011          & -0.024  & 1 \\
\setrow{\bfseries\boldmath} MF cepstral coeff. 1  & 0.98 & 0.77 & $<10^{-100}$ & 0.92 & 0.28 & 0.32 & 0.12  & $4.9*10^{-19}$ & 0.026   & 0.97 \\
                            MF cepstral coeff. 4  & 0.99 & 0.93 & $<10^{-100}$ & 0.97 & 0.17 & 0.29 & 0.12  & $2.5*10^{-10}$ & 0.067   & 0.97 \\
                            MF cepstral coeff. 7  & 0.97 & 0.75 & $<10^{-100}$ & 0.9  & 0.31 & 0.24 & 0.084 & $1.7*10^{-8}$  & 0.019   & 0.99 \\
                            MF cepstral coeff. 10 & 0.97 & 0.69 & $<10^{-100}$ & 0.87 & 0.35 & 0.23 & 0.047 & 0.00013        & 0.048   & 0.97 \\
                            MF cepstral coeff. 13 & 0.99 & 0.93 & $<10^{-100}$ & 0.97 & 0.17 & 0.25 & 0.1   & $1.1*10^{-11}$ & -0.0045 & 0.99 \\
                            Spikiness             & 0.99 & 0.92 & $<10^{-100}$ & 0.97 & 0.18 & 0.22 & 0.073 & $3*10^{-6}$    & 0.025   & 0.98 \\
                            Temporal roughness    & 0.99 & 0.94 & $<10^{-100}$ & 0.98 & 0.14 & 0.18 & 0.062 & 0.011          & -0.037  & 0.99 \\
\setrow{\bfseries\boldmath} Waviness              & 0.99 & 0.94 & $<10^{-100}$ & 0.97 & 0.16 & 0.36 & 0.14  & $3.8*10^{-13}$ & 0.12    & 0.93 \\
                            Regularity            & 0.96 & 0.72 & $<10^{-100}$ & 0.88 & 0.35 & 0.12 & 0.026 & 0.18           & -0.043  & 1 \\
                            Fineness              & 0.99 & 0.94 & $<10^{-100}$ & 0.98 & 0.14 & 0.28 & 0.1   & $4.1*10^{-5}$  & 0.055   & 0.97 \\
                            Friction              & 0.99 & 0.92 & $<10^{-100}$ & 0.97 & 0.18 & 0.2  & 0.066 & 0.003          & -0.05   & 1 \\
                            Friction (normalized) & 0.98 & 0.87 & $<10^{-100}$ & 0.95 & 0.23 & 0.1  & 0.034 & 0.11           & -0.06   & 1 \\
                            \hline \multicolumn{10}{c}{} \\
                            \multirow{2}{*}{Rating}   & \multicolumn{5}{c|}{Training set performance} & \multicolumn{5}{c}{Test set performance} \\
                                                      & \%  & $p$          & $P$   & $R$ & MAE    & \% & $p$  & $P$  & $R$  & MAE  \\ \hline
                            Hardness                  & 100 & $<10^{-100}$ & 1     & 1   & 0      & 67 & 1    & 0.2  & 0.58 & 0.71 \\
\setrow{\bfseries\boldmath} Roughness                 & 100 & $<10^{-100}$ & 0.99  & 1   & 0.0025 & 49 & 0.07 & 0.33 & 0.43 & 0.61 \\
                            Warmness                  & 100 & $<10^{-100}$ & 1     & 1   & 0      & 77 & 1    & 0.25 & 0.77 & 0.26 \\
                            Stickiness (tooling ball) & 100 & $<10^{-100}$ & 1     & 1   & 0      & 63 & 1    & 0.25 & 0.55 & 0.48 \\
                            Stickiness (OptoForce)    & 100 & $<10^{-100}$ & 1     & 1   & 0      & 37 & 0.18 & 0.25 & 0.34 & 0.81 \\
                            Stickiness (BioTac)       & 100 & $<10^{-100}$ & 1     & 1   & 0.0031 & 37 & 0.31 & 0.32 & 0.31 & 0.86 \\
                        \end{tabular}
                        \tblcap{nofs}{cv}
                        \label{tbl:results_nofs_cv}
                    \end{table}