# A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes
## 4 Experimental Results
### Comparison of Improvements in Calibrated Confidence in Document Classification  
The purpose of this subsection is to verify that the proposed method can calibrate the confidence of document classification through experiments on Hypothesis 3. For this, we generate \(L\) times the data labeled \(Y\) and merge it with the training data. The optimal value of \(L\) was determined as shown in Appendix B. Table A1 in Appendix B compares the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Generative** \\ **Model** \\ \end{tabular}  } & \multirow{2}{*}{**Predictors**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Litigation**} & \multicolumn{2}{c}{**Valuation**} & \multicolumn{2}{c}{**Transfer**} \\ \cline{3-8}  & & & **Statistics** & \(p\)**-Value** & **Statistics** & \(p\)**-Value** & **Statistics** & \(p\)**-Value** \\ \hline \multirow{8}{*}{VAE} & \multirow{4}{*}{Quant} & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.117 & 0.833 & 0.106 & 0.013 & 0.018 & \textgreater{}0.500 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,2},\,z^{N}_{i,\,2}\end{pmatrix} \) & 0.148 & \textgreater{}0.500 & 0.106 & 0.013 & 0.017 & \textgreater{}0.500 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\,\cdot}\end{pmatrix} \) & 0.149 & \textgreater{}0.500 & 0.106 & 0.026 & 0.017 & \textgreater{}0.500 \\ \cline{2-8}  & \multirow{2}{*}{Text} & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.141 & \textless{}0.001 & 0.071 & 0.210 & 0.045 & 0.339 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,2},\,z^{N}_{i,\,2}\end{pmatrix} \) & 0.141 & \textless{}0.001 & 0.075 & 0.157 & 0.038 & \textgreater{}0.500 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\,\cdot}\end{pmatrix} \) & 0.141 & \textless{}0.001 & 0.114 & 0.010 & 0.056 & 0.321 \\ \cline{2-8}  & \multirow{2}{*}{\ \begin{tabular}{c} Quant + \\ Text \\ \end{tabular}  } & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.170 & 0.404 & 0.058 & 0.432 & 0.073 & 0.020 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,2},\,z^{N}_{i,\,2}\end{pmatrix} \) & 0.226 & 0.124 & 0.105 & 0.015 & 0.060 & 0.083 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\,\cdot}\end{pmatrix} \) & 0.267 & 0.071 & 0.113 & 0.009 & 0.102 & 0.001 \\ \hline \multirow{8}{*}{C-VAE} & \multirow{4}{*}{Quant} & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.263 & 0.047 & 0.008 & \textgreater{}0.500 & 0.267 & \textless{}0.001 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,2},\,z^{N}_{i,\,2}\end{pmatrix} \) & 0.308 & 0.012 & 0.012 & \textgreater{}0.500 & 0.259 & \textless{}0.001 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\,\cdot}\end{pmatrix} \) & 0.308 & 0.018 & 0.014 & \textgreater{}0.500 & 0.277 & \textless{}0.001 \\ \cline{2-8}  & \multirow{2}{*}{Text} & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.200 & 0.221 & 0.354 & \textless{}0.001 & 0.171 & \textless{}0.001 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\,\cdot}\end{pmatrix} \) & 0.378 & 0.001 & 0.342 & \textless{}0.001 & 0.197 & \textless{}0.001 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\cdot}\end{pmatrix} \) & 0.385 & 0.002 & 0.470 & \textless{}0.001 & 0.232 & \textless{}0.001 \\ \cline{2-8}  & \multirow{2}{*}{ \begin{tabular}{c} Quant + \\ Text \\ \end{tabular}  } & \( \begin{pmatrix}z^{Y}_{i,\,1},\,z^{N}_{i,\,1}\end{pmatrix} \) & 0.646 & \textless{}0.001 & 0.421 & \textless{}0.001 & 0.156 & \textless{}0.001 \\  & & \( \begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{N}_{i,\cdot}\end{pmatrix} \) & 0.588 & \textless{}0.001 & 0.177 & \textless{}0.001 & 0.155 & \textless{}0.001 \\  & & \(\begin{pmatrix}z^{Y}_{i,\,\cdot},\,z^{prediction performance obtained using the proposed method. The optimal values of \(L\) are 1.5, 1.3, and 2.0 for cases where the target variables are _Litigation, Valuation_, and _Transfer_, respectively. For example, when the target variable is _Valuation_, the optimal value of \(L\) is 1.3.