Predicting Credit Card Fraud using Support Vector Machine
What is the purpose of the kernel trick in SVM?
Which metric is most important when evaluating a fraud detection model?
What does a high regularization parameter (C) in SVM signify?
In the context of SVM, what are support vectors?
Why is RBF kernel often preferred in fraud detection over linear kernel?