# Challenges of Using Synthetic Data Generation Methods for Tabular Microdata
## 4 Challenges
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Epochs** & **Batch Size** & **AP** & **AQ** & **BC** & **MB** & **MU** \\ \hline Original & - & - & 0.782 \(\pm\) 0.003 & 0.896 \(\pm\) 0.005 & 0.880 \(\pm\) 0.006 & 0.586 \(\pm\) 0.031 & 0.831 \(\pm\) 0.010 \\ \hline \multirow{4}{*}{CTGAN} & \multirow{3}{*}{300} & 60 & 0.362 \(\pm\) 0.060 & 0.148 \(\pm\) 0.057 & 0.840 \(\pm\) 0.019 & \(-\)0.028 \(\pm\) 0.017 & 0.483 \(\pm\) 0.018 \\  & & 500 & \(-\)1.721 \(\pm\) 0.412 & \(-\)0.037 \(\pm\) 0.147 & \(-\)0.341 \(\pm\) 0.148 & \(-\)0.016 \(\pm\) 0.020 & 0.430 \(\pm\) 0.047 \\ \cline{2-8}  & \multirow{3}{*}{1000} & 60 & 0.501 \(\pm\) 0.043 & 0.381 \(\pm\) 0.049 & 0.839 \(\pm\) 0.021 & \(-\)0.017 \(\pm\) 0.024 & 0.492 \(\pm\) 0.011 \\  & & 500 & 0.562 \(\pm\) 0.059 & \(-\)0.603 \(\pm\) 0.194 & 0.824 \(\pm\) 0.018 & \(-\)0.031 \(\pm\) 0.019 & 0.526 \(\pm\) 0.046 \\ \cline{2-8}  & \multirow{3}{*}{10,000} & 60 & 0.629 \(\pm\) 0.023 & 0.656 \(\pm\) 0.029 & 0.877 \(\pm\) 0.030 & DNF & 0.732 \(\pm\) 0.030 \\  & & 500 & 0.662 \(\pm\) 0.021 & 0.547 \(\pm\) 0.036 & 0.896 \(\pm\) 0.015 & \(-\)0.019 \(\pm\) 0.032 & 0.672 \(\pm\) 0.031 \\ \hline \multirow{4}{*}{TVAE} & \multirow{3}{*}{300} & 60 & 0.702 \(\pm\) 0.011 & 0.363 \(\pm\) 0.039 & 0.899 \(\pm\) 0.019 & 0.298 \(\pm\) 0.036 & 0.721 \(\pm\) 0.032 \\  & & 500 & 0.697 \(\pm\) 0.011 & 0.359 \(\pm\) 0.039 & 0.892 \(\pm\) 0.017 & 0.118 \(\pm\) 0.034 & 0.730 \(\pm\) 0.027 \\ \cline{2-8}  & \multirow{3}{*}{1000} & 60 & 0.707 \(\pm\) 0.014 & 0.535 \(\pm\) 0.033 & 0.890 \(\pm\) 0.021 & 0.412 \(\pm\) 0.034 & 0.722 \(\pm\) 0.031 \\  & & 500 & 0.669 \(\pm\) 0.012 & 0.547 \(\pm\) 0.034 & 0.897 \(\pm\) 0.019 & 0.348 \(\pm\) 0.036 & 0.730 \(\pm\) 0.027 \\ \cline{2-8}  & \multirow{3}{*}{10,000} & 60 & 0.706 \(\pm\) 0.018 & 0.671 \(\pm\) 0.029 & 0.880 \(\pm\) 0.023 & DNF & 0.712 \(\pm\) 0.033 \\  & & 500 & 0.699 \(\pm\) 0.013 & 0.656 \(\pm\) 0.026 & 0.887 \(\pm\) 0.022 & 0.478 \(\pm\) 0.033 & 0.712 \(\pm\) 0.036 \\ \hline \multirow{4}{*}{Copula} & \multirow{3}{*}{300} & 60 & 0.524 \(\pm\) 0.031 & \(-\)0.069 \(\pm\) 0.111 & 0.797 \(\pm\) 0.038 & \(-\)0.031 \(\pm\) 0.018 & 0.492 \(\pm\) 0.021 \\  & & 500 & 0.082 \(\pm\) 0.126 & \(-\)0.212 \(\pm\) 0.167 & \(-\)0.385 \(\pm\) 0.012 & \(-\)0.029 \(\pm\) 0.019 & 0.531 \(\pm\) 0.043 \\ \cline{2-8}  & \multirow{3}{*}{1000} & 60 & 0.463 \(\pm\) 0.041 & 0.388 \(\pm\) 0.054 & 0.812 \(\pm\) 0.036 & \(-\)0.029 \(\pm\) 0.020 & 0.541 \(\pm\) 0.033 \\ \cline{2-8}  & & 500 & 0.612 \(\pm\) 0.027 & 0.087 \(\pm\) 0.121 & 0.649 \(\pm\) 0.082 & \(-\)0.036 \(\pm\) 0.023 & 0.542 \(\pm\) 0.034 \\ \cline{2-8}  & \multirow{3}{*}{10,000} & 60 & 0.671 \(\pm\) 0.022 & 0.682 \(\pm\) 0.029 & 0.863 \(\pm\) 0.040 & DNF & 0.703 \(\pm\) 0.032 \\ \cline{2-8}  & & 500 & 0.672 \(\pm\) 0.017 & 0.571 \(\pm\) 0.031 & 0.814 \(\pm\) 0.037 & \(-\)0.030 \(\pm\) 0.024 & 0.572 \(\pm\) 0.022 \\ \hline Copula & - & - & 0.400 \(\pm\) 0.056 & 0.808 \(\pm\) 0.021 & 0.604 \(\pm\) 0.072 & 0.285 \(\pm\) 0.042 & 0.116 \(\pm\) 0.055 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RF-utility results across various SDG models and epochs/batch sizes on the UCI Machine Learning datasets. implementing these strategies, it may be possible to overcome the scalability limitations currently hindering the broader application of SDG in complex and diverse datasets. In our analysis, significant differences were observed between the high-dimensional datasets (MU and MB) and the other datasets (AP, AQ, BC). The MB dataset, in particular, posed considerable challenges, as evidenced by its low baseline performance. This complexity was reflected in the generally poor performance of most SDG models, with TVAE being a notable exception. TVAE showed relatively robust results, especially when dealing with a high number of categorical variables.