Given a two-feature dataset (account age and transaction month) for labeling behavior as fraudulent or normal, which of the following models is most likely to achieve the highest recall for the fraudulent class?

A decision tree is most likely to achieve the highest recall for the fraudulent class because it can learn complex, non-linear decision boundaries that are often present in fraud detection datasets. Fraudulent transactions are typically a minority class and may not be linearly separable from normal transactions. Decision trees can effectively partition the feature space to isolate these rare events, leading to a higher true positive rate (recall) for the fraudulent class. Linear SVMs and single perceptrons with sigmoid activation are linear classifiers. They struggle with non-linear relationships and may misclassify many fraudulent instances if the data is not linearly separable, resulting in lower recall. Naive Bayes classifiers assume feature independence, which is often violated in real-world fraud data, leading to suboptimal performance and potentially lower recall.