Fig. 4

Scatterplot of algorithm performance. Scatterplot representing the performance of the ML algorithms. The x-axis, labeled “ACC,” measures the accuracy of the algorithms. Accuracy is a simple metric for binary classification problems, representing the proportion of true results (both true positives and true negatives) among the total number of cases examined. The y-axis, labeled “AUC,” represents the area under the receiver operating characteristic (ROC) curve. AUC is a popular metric in machine learning for binary classification problems. It measures the tradeoff between a true-positive rate and a false-positive rate. An AUC of 1.0 means the model has a perfect classification, while an AUC of 0.5 implies the model is no better than random guessing. Each point in the scatterplot represents a different machine learning algorithm. The position of the point on the graph shows the performance of the algorithm on both metrics: its accuracy and its AUC score. The scatterplot also features a performance target of 0.8. This could be represented as a line or a highlighted area in the plot, indicating the desired minimum performance level for both the accuracy and AUC. Algorithms that fall within or above this target region are considered to meet or exceed the performance goal. This visual comparison makes it easier to quickly identify which algorithms meet the performance target according to these two key metrics