## 2 Materials and Methods
### Methodology
#### 2.1.4 Modeling  
When conducting a detailed analysis of the data source, it was determined that the classification output resulting from the motor competence tests is labeled as high, normal, and low. For this reason, the decision was made that the most appropriate type of prediction would be classification. To do this, a comparison of popular supervised machine-learning techniques for a classification model will be conducted. According to the literature, the most used algorithms and optimizers are:  
1. Decision tree: A non-parametric supervised technique that constructs a classification model as a tree structure, applicable for classification and regression tasks [24]. 2. Random forest: It generates a set of decision trees by employing random resampling on the training set [25]. 3. Support vector machine: Creates effective boundaries to separate datasets by solving a constrained quadratic optimization problem [26]. 4. Naive Bayes: It is a probabilistic classifier based on [PERSON] theorem, assuming strong independence within attributes of an instance [27]. 5. Logistic regression: This type of regression analysis is used to predict the outcome of a categorical variable based on the independent or predictor variables [28]. While commonly recognized as a classifier, logistic regression can also be employed as a regressor to predict numeric values. Its adaptability allows it to address classification and regression problems, depending on the nature of the data and the analysis objectives [28]. 6. Neuronal network: Most current neural network applications are concerned with pattern recognition problems. Artificial neural networks consist of assemblies of perceptrons designed for multi-layer feedforward networks [29]. 7. K-nearest neighbors: It seeks to predict outputs by computing the distance between the test data and training points, subsequently selecting the K number of points closest to the test data [30]. 8. Gradient boosted: This ensemble learning technique builds and combines several weak learning models to form a more robust model. The main idea is to correct the errors of the previous model by iteratively adding soft models. It focuses on fitting the residuals of the previous model using a gradient-based approach [31]. 9. XGBoost: short for \"eXtreme Gradient Boosting\", is a specific implementation of gradient boosting. It was developed to be fast and efficient in terms of resource usage. It includes regularization, missing value handling, and a custom cost function [32]. 10. LightGBM: Gradient boosting machines build sequential decision trees, with each tree constructed based on the errors of the preceding tree. In the end, predictions are made by summing the contributions of all these trees. 11. CatBoost: CatBoost stands for \"Category\" and \"Boost\"; it handles categorical, numeric, and text features. The CatBoost algorithm employs a symmetric tree or an oblivious tree structure [33]. Anaconda Navigator Software 2.5.1 was used with its Jupyter Notebook 6.5.2 with the Python 3 programming language and its Scikit-learn optimization library to compare supervised machine-learning techniques for classification. With careful preparation of the input data, it will be imported in a specific CSV format, representing it as a table with the attributes selected in the \"Data selection\" section. Figure 2 shows the modeling developed to classify motor competence data, using information generated by smart bands. This encompasses data processing, modeling, comparisons with machine-learning algorithms, and achieving classification with optimization using hyperparameters in the study. The percentiles were constructed using the LMS method [34]. The curves L represent skewness (lambda), M represents the median (mu), and S represents the coefficient of variation (sigma). The LMS method uses the Box-Cox transformation to fit the data distribution to a normal distribution by minimizing the effects of skewness.