# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 4 Computational Study
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dep. Variable:** & \(R^{2}\) & **Adj. \(R^{2}\)** & \(F\) & \(\text{Pr}(F)\) & \(\text{log}\,L\) & \\ \hline Weighted F-beta & \(0.036\) & \(0.031\) & \(5.329\) & \(0.00125\) & \(496.06\) & \\ \hline \(\hat{\beta}\) & **SE** & \(z\) & \(\text{Pr}(>|z|)\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.9327\) & \(0.017\) & \(54.692\) & \(0.000\) & \(0.899\) & \(0.966\) \\
**CombLength** & \(-0.0017\) & \(0.004\) & \(-0.404\) & \(0.686\) & \(-0.010\) & \(0.007\) \\
**Group[T.Proposed]** & \(-0.1184\) & \(0.044\) & \(-2.697\) & \(0.007\) & \(-0.205\) & \(-0.032\) \\
**CombLength:Group[T.Proposed]** & \(0.0205\) & \(0.011\) & \(1.838\) & \(0.066\) & \(-0.001\) & \(0.042\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GLS statistics for weighted F-beta in binary classification. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dep. Variable:** & \(R^{2}\) & **Adj. \(R^{2}\)** & \(F\) & \(\text{Pr}(F)\) & \(\text{log}\,L\) & \\ \hline Weighted F-beta & \(0.036\) & \(0.031\) & \(5.329\) & \(0.00125\) & \(496.06\) & \\ \hline \(\hat{\beta}\) & **SE** & \(z\) & \(\text{Pr}(>|z|)\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.9327\) & \(0.017\) & \(54.692\) & \(0.000\) & \(0.899\) & \(0.966\) \\
**CombLength** & \(-0.0017\) & \(0.004\) & \(-0.404\) & \(0.686\) & \(-0.010\) & \(0.007\) \\
**Group[T.Proposed]** & \(-0.1184\) & \(0.044\) & \(-2.697\) & \(0.007\) & \(-0.205\) & \(-0.032\) \\
**CombLength:Group[T.Proposed]** & \(0.0205\) & \(0.011\) & \(1.838\) & \(0.066\) & \(-0.001\) & \(0.042\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: GLS statistics for weighted F-beta in multi-class classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dep. Variable:** & \(R^{2}\) & **Adj. \(R^{2}\)** & \(F\) & \(\text{Pr}(F)\) & \(\text{log}\,L\) & \\ \hline Weighted Recall & \(0.034\) & \(0.029\) & \(5.073\) & \(0.00178\) & \(511.84\) & \\ \hline \(\hat{\beta}\) & **SE** & \(z\) & \(\text{Pr}(>|z|)\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.9336\) & \(0.017\) & \(56.180\) & \(0.000\) & \(0.901\) & \(0.966\) \\
**CombLength** & \(-0.0014\) & \(0.004\) & \(-0.344\) & \(0.731\) & \(-0.010\) & \(0.007\) \\
**Group[T.Proposed]** & \(-0.1117\) & \(0.043\) & \(-2.605\) & \(0.009\) & \(-0.196\) & \(-0.028\) \\
**CombLength:Group[T.Proposed]** & \(0.0193\) & \(0.011\) & \(1.767\) & \(0.077\) & \(-0.002\) & \(0.041\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GLS statistics for weighted recall in binary classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dep. Variable:** & \(R^{2}\) & **Adj. \(R^{2}\)** & \(F\) & \(\text{Pr}(F)\) & \(\text{log}\,L\) & \\ \hline \(\hat{\beta}\) & **SE** & \(z\) & \(\text{Pr}(>|z|)\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.7937\) & \(0.032\) & \(25.172\) & \(0.000\) & \(0.732\) & \(0.855\) \\
**CombLength** & \(0.0044\) & \(0.008\) & \(0.544\) & \(0.586\) & \(-0.011\) & \(0.020\) \\
**Group[T.Proposed]** & \(-0.2599\) & \(0.086\) & \(-3.012\) & \(0.003\) & \(-0.429\) & \(-0.091\) \\
**CombLength:Group[T.Proposed]** & \(0.0440\) & \(0.022\) & \(1.962\) & \(0.050\) & \(5.33\times 10^{-5}\) & \(0.088\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: GLS statistics for elapsed time in binary classification.