## 4 Discussion  
This study investigated the use of ML models to classify mental fatigue and recovery states in U15, U17, and U19 male football young sub-elite football players, emphasizing the multi-class classification approach and the integration of positional-specific data. The findings confirm that wearable-derived variables are effective in predicting RPE, with ML models providing valuable insights into athlete workload and recovery management. By analyzing recovery-related metrics, the study aims to identify key features associated with optimal recovery, leveraging these insights to develop personalized strategies that address the unique needs of young athletes during critical stages of their physical and psychological development. Initially, all features from the raw dataset were included in an exploratory step to implement the algorithms, resulting in top performances for XGBoost, RF, and DT classifiers, each achieving a range of accuracy values over several iterations (i.e., 30% to 35%). To improve the model's performance, feature selection was employed to reduce data dimensionality. While the correlation analysis did not establish statistical significance for most features, variables such as TD, rHSR, HMLD, and DSL have been widely acknowledged in sports science literature for their impact on perceived exertion. Therefore, despite their lack of direct correlation in this dataset, their inclusion ensures that the model can capture broader training-related patterns that may influence RPE. These features were deemed most relevant for classifying the recovery states of the sub-elite young football players [24,25]. The evaluation of ML models for predicting RPE demonstrated that the DT classifier outperformed other algorithms, achieving the highest accuracy (32.65%), precision (31.83%), \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Metric** \\ \end{tabular} \\ \hline KNN & 20.41 & 15.54 & 20.41 & 17.44 & 18.45 \\ \hline XGBoost & 25.51 & 24.72 & 25.51 & 24.30 & 25.01 \\ \hline SVM & 23.47 & 13.68 & 23.47 & 16.73 & 19.34 \\ \hline RF & 28.57 & 25.31 & 28.57 & 25.86 & 27.08 \\ \hline DT & 32.65 & 31.83 & 32.65 & 31.67 & 32.20 \\ \hline \end{tabular} Abbreviations: DT—Decision Tree Classifier; KNN—K-Nearest Neighbors; RF—Random Forest Classifier; SVM—Support Vector Machine; XGBoost—Gradient Boosting Classifier.  
\end{table} 
Table 2: Algorithm’s performance in predicting RPE. recall (32.65%), and F1-score (31.67%). The superior performance of the DT model suggests its effectiveness in handling non-linear relationships within the dataset, making it the most suitable algorithm for RPE prediction in this training weekly microcyles. These methods proficiently address the intricacy and variation inherent in the psychophysiological features data obtained from wearable sensors [24; 25]. The consistent underperformance of the KNN algorithm, however, suggests that it may be less effective in dealing with the high-dimensional and potentially noisy nature of the data. Among the other models tested, RF and XGBoost exhibited moderate predictive performance, with accuracy scores of 28.57% and 25.51%, respectively. These ensemble-based models generally perform well with structured data; however, their lower accuracy compared to the Decision Tree suggests that the dataset may not contain enough complexity to benefit from ensemble learning techniques. Additionally, KNN and SVM demonstrated the lowest predictive performance, indicating that distance-based and margin-based learning methods may not be well-suited for RPE prediction in this dataset. The feature selection results highlight the critical role of specific physiological and performance metrics in determining recovery states.