## 3 Results  
After training all the model types available in ML-NET, the best models found after each training session were instances of the LightGBM model. The experiment was then limited to training only LightGBM instances. Table 1 compares the best results obtained in scenarios 1-4. \(\overline{FM}_{D}\) represents the mean value for all the datasets, \(\overline{FM}_{I}\) represents the mean \(FM\) value for all the images, and \(\overline{FM}_{O}\) represents the mean value for all the images after retraining the model on all datasets. \(\overline{FM}_{D}\) and \(\overline{FM}_{I}\) differ because the datasets did not contain the same number of images. \(\overline{FM}_{I}\) and \(\overline{FM}_{O}\) differ because \(\overline{FM}_{I}\) was the average obtained using the results of the cross-validation models for their respective test datasets, whereas \(\overline{FM}_{O}\) was obtained using a single model trained and tested on all the datasets. The importance of each feature used in a decision tree ensemble can be determined by permuting the values of that feature in the dataset and measuring the impact on the evaluation metric [42]. Table 2 shows the impact on \(FM\) of permuting each feature used in the four scenarios. Higher absolute values correspond to more important features and values closer to 0 correspond to less important features. It may be noted that the features exclusive to scenario 4 have significantly lower importance compared with the others, which explains the very small difference between the \(FM\) values obtained in scenarios 3 and 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\(FM\) (\%)** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline \(\overline{FM}_{D}\) & 91.775 & 94.3657 & 95.846 & 95.675 \\ \hline \(\overline{FM}_{I}\) & 91.774 & 94.3652 & 95.845 & 95.672 \\ \hline \(\overline{FM}_{O}\) & 95.357 & 96.92 & 99.41 & 99.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scenario comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **\(FM\) Difference** & **Scenarios Using the Feature** \\ \hline Pixel Value & \(-0.76909\) & \(1,2,3,4\) \\ \hline [PERSON] & \(-0.02847\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.04733\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.07923\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.0711\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.05156\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.07106\) & \(1,3,4\) \\ \hline [PERSON] & \(-0.04388\) & \(1,3,4\) \\ \hline Gaussian Blur & \(-0.38731\) & \(1,3,4\) \\ \hline Average of Local Minimum & \(-0.05427\) & \(1,3,4\) \\ and Maximum & \(-0.05427\) & \(1,3,4\) \\ \hline Local Average & \(-0.01125\) & \(1,2,3,4\) \\ \hline Local Standard Deviation & \(-0.14666\) & \(2,3,4\) \\ \hline Local Skewness & \(-0.08837\) & \(2,3,4\) \\ \hline Local Kurtosis & \(-0.07781\) & \(2,3,4\) \\ \hline Local BC & \(-0.14658\) & \(2,3,4\) \\ \hline Local Minimum & \(-0.15828\) & \(2,3,4\) \\ \hline Local Maximum & \(-0.06222\) & \(2,3,4\) \\ \hline Global Average & \(-0.02384\) & \(2,3,4\) \\ \hline Global Standard Deviation & \(-0.02445\) & \(2,3,4\) \\ \hline Global Skewness & \(-0.04725\) & \(2,3,4\) \\ \hline Global Kurtosis & \(-0.00972\) & \(2,3,4\) \\ \hline Global BC & \(-0.01169\) & \(2,3,4\) \\ \hline Global Minimum & \(-0.00799\) & \(2,3,4\) \\ \hline Global Maximum & \(-0.00867\) & \(2,3,4\) \\ \hline First Bin of Global Histogram & \(-0.05454\) & \(4\) \\ \hline Second Bin of Global Histogram & \(-0.01896\) & \(4\) \\ \hline Third Bin of Global Histogram & \(-0.01805\) & \(4\) \\ \hline Fourth Bin of Global Histogram & \(-0.01445\) & \(4\) \\ \hline Fifth Bin of Global Histogram & \(-0.00761\) & \(4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature importance. Retraining the model on all the datasets significantly improved the average \(FM\), but this might have been caused either by overfitting or by increasing the size of the training set.