Commit 175e9d39 authored by ehebrard's avatar ehebrard
Browse files

geom avg

parent 151bdb1f
......@@ -1364,6 +1364,8 @@ When, $\numfeat$ grows, however, it often exceeds the memory limit of 50GB (wher
% instead of absolute values, we provide the average relative difference in error and accuracy w.r.t. \budalg, however, and only for the data sets where a decision tree was found. Similarly, we report the average cpu time ratio w.r.t. \budalg, however, only for instances which were proven optimal by both algorithms\footnote{every instance proven optimal by \dleight is also proven optimal by \budalg and \murtree}.
% \clearpage
\begin{table}[t]
\begin{center}
\begin{footnotesize}
......@@ -1375,6 +1377,16 @@ When, $\numfeat$ grows, however, it often exceeds the memory limit of 50GB (wher
\end{table}
\begin{table}[t]
\begin{center}
\begin{footnotesize}
\tabcolsep=3.5pt
\input{src/tables/summaryclassesgerror.tex}
\end{footnotesize}
\end{center}
\caption{\label{tab:summaryaccsmall} Comparison with the state of the art, errors are geometric averages}
\end{table}
\begin{table}[t]
\begin{center}
\begin{footnotesize}
......@@ -1386,6 +1398,10 @@ When, $\numfeat$ grows, however, it often exceeds the memory limit of 50GB (wher
\end{table}
% \clearpage
\subsection{Computing accurate classifiers efficiently}
......@@ -1422,15 +1438,15 @@ We can see in those graphs that \murtree finds an initial tree extremely quickly
\begin{figure}
\subfloat[depth=3]{\input{src/tables/xscerror3.tex}}
\subfloat[depth=7]{\input{src/tables/xscerror7.tex}}
\subfloat[depth=10]{\input{src/tables/xscerror10.tex}}
% \subfloat[depth=8]{\input{src/tables/xscerror8.tex}}
% \subfloat[depth=9]{\input{src/tables/xscerror9.tex}}
% \subfloat[depth=10]{\input{src/tables/xscerror10.tex}}
\caption{\label{fig:cactus}Accuracy over time, averaged across all data sets}
\end{figure}
% \begin{figure}
% \subfloat[depth=3]{\input{src/tables/xscerror3.tex}}
% \subfloat[depth=7]{\input{src/tables/xscerror7.tex}}
% \subfloat[depth=10]{\input{src/tables/xscerror10.tex}}
% % \subfloat[depth=8]{\input{src/tables/xscerror8.tex}}
% % \subfloat[depth=9]{\input{src/tables/xscerror9.tex}}
% % \subfloat[depth=10]{\input{src/tables/xscerror10.tex}}
% \caption{\label{fig:cactus}Accuracy over time, averaged across all data sets}
% \end{figure}
\subsection{Factor analysis}
......
\begin{tabular}{lrrrrrrrrrrrrrrr}
\toprule
\multirow{2}{*}{$\mdepth$}& \multicolumn{3}{c}{\budalg} & \multicolumn{3}{c}{\murtree} & \multicolumn{3}{c}{\cp} & \multicolumn{4}{c}{\dleight} & \multicolumn{2}{c}{\binoct}\\
\cmidrule(rr){2-4}\cmidrule(rr){5-7}\cmidrule(rr){8-10}\cmidrule(rr){11-14}\cmidrule(rr){15-16}
& \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error$^*$} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{error$^*$} \\
\midrule
&\multicolumn{15}{c}{$\numfeat < 100$ (29 data sets)}\\
\midrule
\texttt{3} & 1.00 & 65.4 & 0.23 & 1.00 & 65.4 & $\mathsmaller{+}$0.31 & 1.00 & 65.4 & $\mathsmaller{+}$3.2 & 1.00 & 1.00 & $\mathsmaller{+}$1.0 & $\mathsmaller{+}$2.5 & 0.52 & $\mathsmaller{+}$5.2\\
\texttt{4} & 1.00 & 43.1 & 14 & 1.00 & 43.1 & $\mathsmaller{+}$8.0 & 1.00 & 43.1 & $\mathsmaller{+}$115 & 1.00 & 1.00 & $\mathsmaller{+}$1.0 & $\mathsmaller{+}$105 & 0.52 & $\mathsmaller{+}$17\\
\texttt{5} & 0.93 & 26.4 & 187 & 0.97 & 26.4 & -12 & 0.62 & 26.5 & $\mathsmaller{+}$121 & 0.76 & 0.66 & $\mathsmaller{+}$1.5 & $\mathsmaller{+}$2.0 & 0.52 & $\mathsmaller{+}$27\\
\texttt{7} & 0.66 & 12.2 & 81 & 0.69 & 12.7 & $\mathsmaller{+}$90 & 0.45 & 20.3 & $\mathsmaller{+}$193 & 0.66 & 0.55 & $\mathsmaller{+}$2.7 & $\mathsmaller{+}$6.7 & 0.52 & $\mathsmaller{+}$36\\
\texttt{10} & 0.79 & 7.2 & 85 & 0.52 & 10.0 & $\mathsmaller{+}$83 & 0.45 & 27.2 & $\mathsmaller{+}$2.6 & 0.62 & 0.52 & $\mathsmaller{+}$3.3 & $\mathsmaller{+}$49 & 0.41 & $\mathsmaller{+}$150\\
\midrule
&\multicolumn{15}{c}{$\numfeat \geq 100$ (29 data sets)}\\
\midrule
\texttt{3} & 0.86 & 147.0 & 100 & 0.86 & 147.9 & -42 & 0.72 & 147.5 & $\mathsmaller{+}$256 & 0.76 & 0.66 & $\mathsmaller{+}$1.9 & $\mathsmaller{+}$247 & 0.62 & $\mathsmaller{+}$15\\
\texttt{4} & 0.55 & 98.6 & 662 & 0.72 & 99.6 & $\mathsmaller{+}$64 & 0.28 & 111.4 & $\mathsmaller{+}$576 & 0.48 & 0.24 & $\mathsmaller{+}$11 & $\mathsmaller{+}$258 & 0.62 & $\mathsmaller{+}$31\\
\texttt{5} & 0.34 & 62.7 & 452 & 0.34 & 64.9 & $\mathsmaller{+}$99 & 0.14 & 173.1 & $\mathsmaller{+}$11 & 0.34 & 0.10 & $\mathsmaller{+}$47 & $\mathsmaller{+}$12 & 0.62 & $\mathsmaller{+}$67\\
\texttt{7} & 0.31 & 36.9 & 11 & 0.31 & 49.2 & $\mathsmaller{+}$7.4 & 0.28 & 122.9 & $\mathsmaller{+}$571 & 0.34 & 0.14 & $\mathsmaller{+}$70 & $\mathsmaller{+}$793 & 0.55 & $\mathsmaller{+}$121\\
\texttt{10} & 0.45 & 16.7 & 101 & 0.41 & 22.6 & $\mathsmaller{+}$19 & 0.38 & 66.8 & $\mathsmaller{+}$85 & 0.45 & 0.28 & $\mathsmaller{+}$19 & $\mathsmaller{+}$183 & 0.21 & $\mathsmaller{+}$260\\
\bottomrule
\end{tabular}
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