## 3 Results
The performance of a MLA can be optimized by changing a set of parameters. Each MLA has its own parameter that can be fine-tuned. The six models were improved in the first improvement procedure by using the gridsearch() function [45], which allows bulk testing of models on a selected set of parameters. This procedure is known as hyperparameter tuning. In the second testing stage (accuracy >92%, standard deviation <1.5%), only three models reached these stricter criteria, namely Model 3: RF with Dataset 5 (92.79% accuracy, 1.29% standard deviation, 16.13 s); Model 4: RF with Dataset 6 (92.75% accuracy, 1.35% standard deviation, 15.91 s); and Model 5: XGB combined with Dataset 5 (92.80% accuracy, 1.48% standard deviation, 18.97 s). A side effect of the improvement in accuracy was a rise in computational time during the training (Table 11). In the second improvement procedure, the best models from the second testing stage were analyzed to accomplish feature reduction. A heatmap was used to rank the features of Datasets 5 and 6 and further improve Models 3, 4, and 6. The features of Dataset 5 were reordered as follows: Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), CaO, P\({}_{2}\)O\({}_{5}\), Na\({}_{2}\)O, MnO, SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\)T, MgO, and K\({}_{2}\)O. The features of Dataset 6 were reordered as follows: Al\({}_{2}\)O\({}_{3}\), CaO, Na\({}_{2}\)O, TiO\({}_{2}\), MnO, P\({}_{2}\)O\({}_{5}\), SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\)T, K\({}_{2}\)O, and MgO. After using all the rearranged features, the three models showed minor improvements (Table 12). Model 6 achieved the best results. Rearranging the features resulted in an improvement in computational time. By removing features based on the aforementioned ranking, no increase in accuracy was achieved. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **MLA** & **Dataset** & **Accuracy** & **Std** & **Time (Sec)** & **Parameters** \\ \hline
1 & kNN & 6 & 88.58\% & 1.08 & 3.52 & n\_neighbors = 5, weight = uniform, metric = Euclidean \\
2 & RF & 3 & 91.64\% & 1.24\% & 10.39 & max depth = 3, n\_estimators = 100 \\
3 & RF & 5 & 92.73\% & 1.46\% & 9.64 & max depth = 3, n\_estimators = 100 \\
4 & RF & 6 & 92.69\% & 1.48\% & 9.05 & max depth = 3, n\_estimators = 100 \\
5 & XGB & 1 & 91.57\% & 1.34\% & 13.19 & max depth = 3, n\_estimators = 100, learning\_rate = 0.1 \\
6 & XGB & 5 & 92.75\% & 1.36\% & 11.48 & max depth = 3, n\_estimators = 100, learning\_rate = 0.1 \\ \hline \end{tabular}
\end{table} 
Table 10: Best performing models (first testing stage). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **MLA** & **Dataset** & **Accuracy** & **Std** & **Time (Sec)** & **Parameters** \\ \hline
1 & kNN & 6 & 91.26\% & 1.70\% & 3.21 & n\_neighbors = 2, weights = distance, metric = Manhattan \\
2 & RF & 3 & 91.85\% & 0.95\% & 30.16 & max depth = 26, n\_estimators = 200 \\
3 & RF & 5 & 92.79\% & 1.29\% & 16.13 & max depth = 17, n\_estimators = 200 \\
4 & RF & 6 & 92.75\% & 1.35\% & 15.91 & max depth = 14, n\_estimators = 200 \\
5 & XGB & 1 & 91.93\% & 1.56\% & 23.03 & max depth = 9, n\_estimators = 106, learning\_rate = 0.1 \\
6 & XGB & 5 & 92.80\% & 1.48\% & 18.97 & max depth = 8, n\_estimators = 101, learning\_rate = 0.1 \\ \hline \end{tabular}
\end{table} 
Table 11: Performance improvement of the models after hyperparameter tuning (first improvement procedure). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **MLA** & **Dataset** & **Accuracy** & **Std** & **Time (Sec)** & **Parameters** \\ \hline
3 & RF & 6 & 92.81\% & 1.3\% & 15.28 & max depth = 17, n\_estimators= 200 \\
4 & RF & 5 & 92.90\% & 1.25\% & 14.43 & max depth = 14, n\_estimators= 200 \\
6 & XGB & 5 & 92.94\% & 1.15\% & 13.85 & max depth = 8, n\_estimators= 101, learning\_rate = 0.1 \\ \hline \end{tabular}
\end{table} 
Table 12: Performance improvement of the models after applying a heatmap (second testing stage, second improvement procedure).