## 4 Results
### Model Fitting  
Classification algorithms are supervised learning methods that are widely used to solve predictive modeling problems when the response variable is labeled into classes or categories. Since the response variable considered is dichotomous, this is a binary classification problem (Collision: \"1\"; Avoidance:\"0\"). The accuracy and performance metrics for each of these methods have been calculated using the k-fold cross-validation technique [38], which allows different partitions of data to be selected between the training set and test set, thus ensuring the generation of independent data partitions. By using a total of k = 5 iterations, the possible overfitting derived from a small training data size is avoided, thus obtaining a higher reproducibility in the whole sample and higher reliability in obtaining the accuracy through the arithmetic mean of all k repetitions. Figure 11: Change in total sickness measure of each stage. Figure 12: SUS score frequency distribution across all participants. To obtain the hyperparameters that optimize the overall performance of the models, the GridSearchCV function of the sklearnmodel_selection module is used. Table 7 lists these hyperparameters, which will be described in the following paragraphs. The distance-based models are:  
(1) _Support Vector Machine (SVM)_: This method is based on the construction of a hyperplane or set of hyperplanes in a space of very high (or even infinite) dimensionality to achieve a good separation of classes to obtain a good classification. Therefore, the greater the distance of the hyperplane to the training data points that are closest to each other, the smaller the error made by the classifier [39]. The mathematical functions used in the SVM algorithm, known as Kernel functions, can be linear, polynomial, radial basis function (RBF), sigmoid, etc. These functions assign the data to a different, generally higher dimensional space, intending to separate the classes more simply once the transformation has been carried out and thus simplifying the complex and non-linear decision limits to make them linear in the primitive high-dimensional space [40]. Since the study does not work with linearly separable data, the objective function consists of minimizing the first term, equivalent to maximizing the margin between classes, and a second term, where the error associated with the margin violation is multiplied by a regularization term C. Ideally, the term would be greater than or equal to one, indicating perfect accuracy. However, the introduction of the penalty term C allows controlling the strength of the penalty incurred when a sample is misclassified or within the margin boundary. It is therefore obtained that the highest accuracy values are achieved with a Radial Basis Function (RBF) kernel, Gamma: \(1\times 10^{-3}\), and C = 1000. Note that the value of the regularization parameter C is high enough to state that a lower margin will be accepted if the decision function is better at classifying all the points of the training set correctly. Nevertheless, this high value does not condition the accuracy obtained (86.1%). (2) _K-nearest neighbors (KNN)_: It is a classification method that enables us to estimate the density function of the predictors or observations \(\mathrm{X_{i}}\) for each class \(\mathrm{C_{j}}\) of the response variable or directly the a posteriori probability that this element belongs to the corresponding class. This is a lazy type of learning since the function is approximated only locally, computing from a simple majority vote of the K nearest neighbors of each observation [41]. This is a robust method when there is noise in the training dataset, especially when the value of K is large in that case. Similarly, if the value of K is high, it can create boundaries between similar classes. In this model, the distance metric used is Minkowski, so the distances are calculated with the standard Euclidean metric. Applying the GridSearchCV optimization function, the highest accuracy is obtained for K = 8 (91.5%).