## 6 Automatic Learning Approach  
ML is a subfield of artificial intelligence (AI) concerned with implementing computational algorithms that improve performance based on historical data [53]. An ML strategy consists of three essential components: data, representation, and model. The data refer to previously curated dataset information that has been recompiled, followed by the representation, which is the numerical translation of the input information for use in the model, where the selection of variables or features that will comprise the model input can have a significant impact on the model's performance, and finally, the model, which is the mathematical representation of the process. The model, which can be categorized in different ways, is the mathematical representation of the process (unsupervised, supervised, active, or transfer learning). In general, the term ML can be applied to any method that implicitly models correlations within datasets [54; 55]. Based on the recompiled data, the supervised learning strategy was chosen. This strategy, which involves the ML task of learning a function that translates an input to an output based on example input-output pairs, is supervised learning. It infers a function from a set of training instances labeled with training data. Supervised ML algorithms require external supervision. Train and test datasets are created from the input dataset. The training dataset contains output variables that require prediction or classification. All algorithms extract patterns from the training dataset and apply them to the test dataset for prediction or classification. There are numerous techniques within the supervised learning methodology, including linear regression, logistic regression, K-nearest neighbors (KNN), Naive Bayes, decision trees, and more. However, selecting a technique depends on the desired features and the data type. In this approach, the best-suited technique for the model porpoise was determined by recompiling the crucial information of each technique [56]. The KNN technique was chosen based on the recompiled information about the technique that must correspond to the purpose of the model, which is determining the compatibility or incompatibility of AC and WM compounds, and the available information, which is labeled, noncontinuous, and noise-free. In this manner, the KNN technique was implemented in R, considering a data split of 70% training, 10% validation, and 20% test sets (Figure 8). The model was trained using k-fold cross-validation with hyperparameter tuning to choose the best-performing model. The validation and training sets were used in a data combination to retrain (refit) the model, and the test set was used for the classification model for the compatibility prediction. Initially, the binary data of the variable \"case\" were substituted with the strings \"yes\" and \"no\" to signify whether there was compatibility or incompatibility. The variable was subsequently transformed from a vector into a factor or category. The raw data, consisting of 288 observations and 21 variables, were divided into three datasets: training, validation, and test. The division was performed in a ratio of 70:10:20. To enhance the strength and reliability of the model, k-fold cross-validation and hyperparameter optimization were conducted simultaneously on the training dataset. Figure 9 illustrates the implementation of the k-fold cross-validation, specifically using repeated cross-validation with 10 sets of folds. The data were divided into 10 subsets (folds) for cross-validation, and repeated cross-validation was performed three times. The model underwent training using k-1 folds and was assessed on the remaining fold [57]. The process was iterated three times, with each fold used as the validation set once. A single estimation was obtained by averaging the results from each iteration. This method reduces the variability and offers a more comprehensive performance measurement for the model [58].