## 3 Experiment Setup and ML Tuning Parameters
### Brief Summary of the Selected ML Model  
When employing ML models, tuned parameters are vital in obtaining high-accuracy outcomes. Each classifier has unique tuning steps and customised parameters; therefore, the users must understand the utilised ML model. This section offers a brief description of the ML models, with a focus on their working principles and hyperparameters.  
**(a)**: **K-Nearest Neighbours**: **KNN**: **algorithm relies on classification and clustering as its foundational processes. Rule induction, segmentation, anomaly detection and visualisation are examples of high-level algorithms that use these two processes. The underlying distance metric considerably impacts the model outcome for the clustering and classification techniques. Euclidean distance, longest common subsequence and edit distance with a real penalty are some of the distance metrics used in time series classification and clustering applications, as presented in Figure 2[22]. Setting the hyperparameter right is critical because of the ability of the hyperparameter to improve the distance measurement accuracy. The main hyperparameter value for KNN is the k value, which indicates the count of the nearest neighbours. Setting this parameter to the lowest feasible value whilst considering the measure's quality may minimise the calculation time for certain elastic measures [23]. In addition to its ease of implementation, KNN has the benefit of working well with vast amounts of noisy training data [24]. However, a major drawback is that for every new instance, all the distances from K neighbours must be computed; this step consumes much computing time and resources [25]. Therefore, the k values must be established appropriately for error rate reduction [26].
**(b)**: **Classification and Regression Trees (CART)**: **Predictive ML techniques, such as the decision tree approach, are also widely utilised for classification and regression. The CART model, a variation in the decision tree approach, is represented by a binary tree with split rules at each root node. The split condition is applied to the root node, and the decision procedure is sequentially performed for each subroot node. Each root node is considered to have a single input variable, 'x', representing the variable and any splits on it to visualise the aforementioned scenario well. At the leaves, 'y' is an output variable that predicts the output. The categorical prediction uses the choice of entropy, whereas the continuous prediction relies on the sum of square errors. This method simplifies ML for novice users by providing an easy-to-understand and visible model. It also needs minimal data preprocessing and can handle categoricaland numerical data types. However, the CART model is an unstable paradigm and it may occasionally result in a complicated tree structure that is not sufficiently generalised [27]. The complexity parameter (cp) is utilised as a tuning parameter in the CART model to increase its performance and solve the issues mentioned. It penalises the tree if it has an excessive number of splits; in most cases, the default setting is set at 0.01. In particular, small trees are produced with the increase in the cp value. Figure 3 depicts the final tree generated using the CART model.