Which is NOT a negative aspect of SVM methods?
A) SVM has "black box" aspects which make it less transparent than OLS regression
B) Trial and error methods may be needed to optimize the model
C) In spite of cross-validation it is still possible to overfit SVM models to noise in the data
D) All of the above.

Question

Quizplus · Accepted Answer

All the options listed (A, B, and C) are considered negative aspects of SVM (Support Vector Machines) methods. SVM can be seen as less transparent or a "black box" compared to simpler models like OLS regression, optimizing SVM models often requires trial and error to select the right kernel and parameters, and despite using techniques like cross-validation, it's still possible to overfit SVM models to noise in the data.

Which Is NOT a Negative Aspect of SVM Methods