## 3 Evaluation and Results  
For the Anaconda Navigator platform with Jupyter Notebook, different machine-learning techniques used in further research were modeled [35], which were decision tree, support vector machine, random forest, naive Bayes, logistic regression, k-nearest neighbors, neuronal network, gradient boosted, and smart bands. Of the data to model, 80% was used for training and 20% for testing. Jupyter Notebook is a widely used tool in the machine-learning community, where you can import a set of libraries, train a dataset classification classifier, and evaluate the model by just using a few lines of code in Python with its library Scikit-learn. Likewise, the optimization of hyperparameters with the Scikit-learn optimization library was used. Hyperparameters are parameters that are not directly learned by the learning algorithm. The basic hyperparameter tuning models are manual search, grid search, and random search. Random search was used for training since it allows us to find equal or better models in computing time. The configuration used for the hyperparameters in the case of gradient boosted was the loss function to optimize with the \"log_loss\" option, the criterion with the process to measure the quality of a division with the \"friedman_mse\" option, the \"learning_rate\" with 0.1, \"max_depth\": 3, \"min_samples_leaf\": 1, \"min_samples_split\": 2, \"n_estimators\": 100, \"random_state\": none, \"subsample\": 1.0, \"tol\": 0.0001, \"validation_fraction\": 0.1, and with the other default parameters. Next, the tests were carried out, and the results of the modeling that was executed for the different techniques that were previously chosen were obtained using the CSV file as input data and made up of the motor competence variables. To evaluate the model [36], accuracy in Equation (1), recall in Equation (2), precision in Equation (3), and the f1-score in Equation (4) were used. For many classes \(C_{i},fp_{i}\) represents false positive, \(tp_{i}\) true positive, \(fn_{i}\) false negative, and \(tn_{i}\) true negative. \[Accuracy=\frac{tp_{i}+tn_{i}}{tp_{i}+tn_{i}+fp_{i}+fn_{i}} \tag{1}\]  
\[Recall=\frac{\sum_{i=1}^{l}\frac{tp_{i}}{tp_{i}+fn_{i}}}{l} \tag{2}\]  
Figure 5: Distribution of percentiles for the velocity in schoolchildren in both sexes. Figure 6: Distribution of percentiles for the stride in schoolchildren in both sexes. \[Precision=\frac{\sum_{i=1}^{l}\frac{tp_{i}}{tp_{i}+fp_{i}}}{l} \tag{3}\]  
\[F1-score=2\times\frac{(Precision)\times(Recall)}{(Precision)+(Recall)} \tag{4}\]  
Tables 4 and 5 compare the classical and optimized machine-learning techniques results with selected hyperparameters and the accuracy, f1-score, recall, and precision metrics for both schoolchildren males and females. In the results of the previous tables, it can be identified that, for the classification techniques, those that gave the best results concerning accuracy for the case of males and females were gradient boosting, whose values were the highest, indicating a better adjustment to the estimated prediction with a value of 0.95. For the f1-score metrics, the algorithms gave similar values of 0.92 between males and females. In the case of _recall_, it was 0.92. The confusion matrix of the gradient boosting machine-learning algorithm with the highest score found is shown in Figure 7.  
\begin{table} \begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Algorithm** & **DT** & **SVM** & **RF** & **NB** & **LR** & **KNN** & **MLP** & **GB** & **XGB** & **LGBM** & **CB** \\ \hline Accuracy & 0.88 & 0.69 & 0.92 & 0.74 & 0.78 & 0.81 & 0.68 & 0.93 & 0.92 & 0.94 & 0.93 \\ Accuracy optimized & 0.87 & 0.82 & 0.92 & 0.75 & 0.79 & 0.85 & 0.70 & 0.95 & 0.91 & 0.94 & 0.93 \\ f1-score & 0.87 & 0.74 & 0.93 & 0.77 & 0.82 & 0.88 & 0.72 & 0.92 & 0.91 & 0.93 & 0.92 \\ Recall & 0.86 & 0.67 & 0.94 & 0.73 & 0.84 & 0.88 & 0.67 & 0.92 & 0.90 & 0.94 & 0.92 \\ Precision & 0.88 & 0.82 & 0.92 & 0.82 & 0.80 & 0.88 & 0.77 & 0.92 & 0.92 & 0.92 & 0.92 \\ \hline \hline \end{tabular}