# Machine Learning Regressors to Estimate Continuous Oxygen Uptakees (\(\dot{VO}_{2}\))
## 3 Results
### Continuous \(\dot{V}O_{2}\) Estimation  
In the research paper's Section 3, various regression models were evaluated for their performance in predicting the target variable, \(\dot{V}O_{2}\) mL/kg/min. The table (Table 2) presents a comprehensive comparison of each model's performance metrics, including MAE, MSE, RMSE, \(R^{2}\), RMSLE, MAPE, and execution time (TT in seconds). Among the models assessed, XGBoost exhibited the most promising results, achieving an MAE of 0.1834, MSE of 0.0640, RMSE of 0.2529, and an impressive R-squared value of 0.9996. Importantly, XGBoost exceeded other models in terms of predictive precision and model fit. Further analysis involved fine-tuning the XGBoost model using 5-fold cross-validation and optimizing for MAPE.  
This process resulted in enhanced performance metrics, with the tuned XGBoost model demonstrating a mean MAE of 0.1217 and a mean MAPE of 0.0056 on the validation set. These findings underscore the model's robustness and its ability to generalize well to unseen data. Subsequently, the tuned XGBoost model was evaluated on an independent test dataset, where it maintained its MAE of 0.1793, MSE of 0.1460, and an \(R^{2}\) value of 0.9991, indicating its effectiveness in predicting the \(\dot{V}O_{2}\) for the mL/kg/min variable. Across  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Model (n = 532)** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **RMSLE** & **MAPE** & **TT (s)** \\ \hline Extreme Gradient & 0.1834 & 0.0640 & 0.2529 & 0.9996 & 0.0146 & 0.0077 & 0.4600 \\ Boosting & & & & & & & \\ Light Gradient Boosting & 0.2419 & 0.1274 & 0.3570 & 0.9992 & 0.0169 & 0.0090 & 1.0520 \\ Machine & & & & & & & \\ Gradient Boosting & 0.6158 & 0.6804 & 0.8248 & 0.9959 & 0.0282 & 0.0210 & 3.4760 \\ Regressor & 0.8622 & 1.4428 & 1.2011 & 0.9913 & 0.0929 & 0.0407 & 5.3260 \\ AdaBoost Regressor & 2.2091 & 7.4254 & 2.7245 & 0.9551 & 0.1206 & 0.0944 & 2.4900 \\ \hline Cross Validation & MAE & MSE & RMSE & R\({}^{2}\) & RMSLE & MAPE & \\ \hline
1 & 0.1297 & 0.0377 & 0.1942 & 0.9998 & 0.0137 & 0.0058 & \\
2 & 0.1289 & 0.0379 & 0.1947 & 0.9998 & 0.0142 & 0.0057 & \\
3 & 0.1240 & 0.0335 & 0.1829 & 0.9998 & 0.0129 & 0.0055 & \\
4 & 0.1265 & 0.0349 & 0.1869 & 0.9998 & 0.0136 & 0.0056 & \\
5 & 0.1265 & 0.0373 & 0.1933 & 0.9998 & 0.0124 & 0.0055 & \\ Mean & 0.1271 & 0.0363 & 0.1904 & 0.9998 & 0.0134 & 0.0056 & \\ Standard deviation & 0.0020 & 0.0018 & 0.0047 & 0.0000 & 0.0006 & 0.0001 & \\ \hline Test set (n = 154) & MAE & MSE & RMSE & R\({}^{2}\) & RMSLE & MAPE & \\ \hline Extreme Gradient & 0.1793 & 0.1460 & 0.3821 & 0.9991 & 0.0140 & 0.0066 & \\ Boosting & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various machine learning regressors on the training set, and the performance of XGBoost in cross-validation and on the test set.  
the board, the results validate the efficacy of the XGBoost model in predicting oxygen consumption levels and highlight its potential utility in practical applications requiring precise estimation of \(\dot{V}O_{2}\) in mL/kg/min.