## 3 Results
### Model Optimization and Testing  
Derived from the baseline results, the following four classifiers with the best-performing models (of both classification tasks) were selected for the optimization phase: random forest (RF), k-nearest-neighbor (kNN), support vector classifier (SVC) and multi-layer perceptron (MLP). For the optimization phase, the scikit-learn implementation of grid search [44] was utilized for each classifier with a predefined grid to establish the best-performing hyperparameter configuration. For model comparison and final selection, 10-fold cross-validation was applied to the test portion of the dataset from the data-acquisition experiment to establish comparable accuracy results for the baseline and optimized configuration of each of the four classifiers. The results are shown in Table 5 for the striking technique and in Table 6 for the target object classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Accuracy** & **F1-Score (Weighted)** \\ \hline DT & 90.33 (\(\pm\)1)\% & 90.46 (\(\pm\)1)\% \\ RF & 93.99 (\(\pm\)1)\% & 92.45 (\(\pm\)1)\% \\ NB (Gaussian) & 84.79 (\(\pm\)2)\% & 86.94 (\(\pm\)2)\% \\ kNN & 96.05 (\(\pm\)1)\% & 95.69 (\(\pm\)1)\% \\ MLP & 96.78 (\(\pm\)1)\% & 96.67 (\(\pm\)1)\% \\ SVC & 95.69 (\(\pm\)1)\% & 95.01 (\(\pm\)1)\% \\ Perceptron & 91.42 (\(\pm\)1)\% & 91.19 (\(\pm\)1)\% \\ LR & 93.99 (\(\pm\)1)\% & 93.42 (\(\pm\)1)\% \\ \hline \hline \end{tabular} (DT = Decision tree; NB = Naive Bayes; RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron; LR = Logistic Regression).  
\end{table}
Table 4: Target object baseline classification results computed using 10-fold cross-validation on the training and optimization dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Accuracy** & **F1-Score (Weighted)** \\ \hline DT & 77.53 (\(\pm\)2)\% & 77.50 (\(\pm\)2)\% \\ RF & 90.93 (\(\pm\)2)\% & 90.88 (\(\pm\)2)\% \\ NB (Gaussian) & 73.72 (\(\pm\)3)\% & 72.99 (\(\pm\)3)\% \\ kNN & 90.73 (\(\pm\)2)\% & 90.67 (\(\pm\)2)\% \\ MLP & 90.88 (\(\pm\)2)\% & 90.84 (\(\pm\)2)\% \\ SVC & 91.60 (\(\pm\)1)\% & 91.57 (\(\pm\)1)\% \\ Perceptron & 76.49 (\(\pm\)3)\% & 76.29 (\(\pm\)3)\% \\ LR & 82.28 (\(\pm\)3)\% & 82.17 (\(\pm\)3)\% \\ \hline \hline \end{tabular} (DT = Decision tree; NB = Naive Bayes; RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron; LR = Logistic Regression).  
\end{table}
Table 3: Striking technique baseline classification results computed using 10-fold cross-validation on the training and optimization dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Baseline Accuracy Result** & **Optimized Accuracy Result** \\ \hline RF & 91.76\% & 92.08\% \\ kNN & 90.49\% & 92.87\% \\ MLP & 92.71\% & 92.71\% \\ SVC & 92.71\% & 93.03\% \\ \hline \hline \end{tabular} (RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron).  
\end{table}
Table 5: Predictive performance for the striking technique classification computed using 10-fold cross-validation on the test dataset.