# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 4 Computational Study
Figure 5:4 Distribution of time (in seconds) by feature selection method group and feature set target length (combination_len) for binary classification datasets. The proposed method shows consistently better times than the other approaches group. Performance degrades a little as the feature set length increases, as was expected. Figure 5:5 Distribution of weighted f-beta score by feature selection method group and feature set target length (combination_len) for multi-class classification datasets. The proposed method shows better \(\it{f}\)-beta score as the feature set length increases, eventually becoming competitive with the other approaches group. The full summary statistics for the GLS models are presented in Tables 2-9. The results from the [PERSON]'s \(t\)-tests (reported in Table 10) confirm that the differences observed across several key metrics are statistically significant, particularly with respect to computational time and performance trends as the feature set length increases. For reference, the hardware used to carry out these tests was a setup consisting of an MSI X370 mainboard, an AMD Ryzen 5 5600G CPU with a max clock speed of 4.4 GHz, a NVIDIA GeForce RTX 3070 GPU and 36 GBytes of DDR4 RAM running at 16 MHz. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dep. Variable:** & \(\mathbf{R^{2}}\) & **Adj. \(\mathbf{R^{2}}\)** & \(\mathbf{F}\) & \(\mathbf{\Pr(F)}\) & \(\mathbf{\log L}\) & \\ \hline Weighted Precision & \(0.034\) & \(0.029\) & \(5.128\) & \(0.00165\) & \(494.12\) & \\ \hline  & \(\hat{\beta}\) & **SE** & \(\mathbf{z}\) & \(\mathbf{\Pr(>|z|)}\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.9327\) & \(0.017\) & \(54.549\) & \(0.000\) & \(0.899\) & \(0.966\) \\
**CombLength** & \(-0.0017\) & \(0.004\) & \(-0.398\) & \(0.691\) & \(-0.010\) & \(0.007\) \\
**Group[T.Proposed]** & \(-0.1142\) & \(0.044\) & \(-2.615\) & \(0.009\) & \(-0.200\) & \(-0.029\) \\
**CombLength:Group[T.Proposed]** & \(0.0195\) & \(0.011\) & \(1.756\) & \(0.079\) & \(-0.002\) & \(0.041\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GLS statistics for weighted precision in binary classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dep. Variable:** & \(\mathbf{R^{2}}\) & **Adj. \(\mathbf{R^{2}}\)** & \(\mathbf{F}\) & \(\mathbf{\Pr(F)}\) & \(\mathbf{\log L}\) & \\ \hline Weighted Precision & \(0.028\) & \(0.023\) & \(4.282\) & \(0.00527\) & \(139.90\) & \\ \hline  & \(\hat{\beta}\) & **SE** & \(\mathbf{z}\) & \(\mathbf{\Pr(>|z|)}\) & **[0.025** & **0.975**] \\ \hline
**Intercept** & \(0.8126\) & \(0.030\) & \(27.330\) & \(0.000\) & \(0.754\) & \(0.871\) \\
**CombLength** & \(0.0026\) & \(0.008\) & \(0.339\) & \(0.735\) & \(-0.012\) & \(0.018\) \\
**Group[T.Proposed]** & \(-0.1668\) & \(0.077\) & \(-2.180\) & \(0.029\) & \(-0.317\) & \(-0.017\) \\
**CombLength:Group[T.Proposed]** & \(0.0267\) & \(0.020\) & \(1.321\) & \(0.187\) & \(-0.013\) & \(0.066\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GLS statistics for weighted precision in multi-class classification. Figure 20: Distribution of time (in seconds) by feature selection method group and feature set target length (combination_len) for multi-class classification datasets. The proposed method shows consistently better times than the other approaches group. Performance degrades a little as the feature set length increases, as was expected.