In a recent exam on machine learning I came across the following question:
"Which of the following techniques can model the decision boundary depicted in the figure? (check all that apply)" See my self-made picture.
- Logistic regression (with linear features)
- Neural Networks
- Naive Bayes
- Support vector machine (with linear Kernel)
I was convinced the answer is Neural Networks and Naive Bayes. In particular Gaussian Naive Bayes can model circular decision boundaries (see an example here; it works, I also tried it myself).
However I have been told Naive Bayes was not rated as correct. Instead only neural nets was the correct answer.
How is this possible or is it a mistake?