Figure 1 shows a sample data including the two classes. Both logistic regression and Naive Bayes methods can be applied to classify the data without any error. One reason is that there is a gap area known as a decision boundary that logistic regression and Naive Bayes can search for to separate the two classes. Figure 2 illustrates a decision boundary that can be used for classification.
Finding a decision boundary can difficult in some other dataset. If logistic regression or Naive Bayes cannot find a correct decision boundary, their classification results would involve a serious error. Can you provide an example of 2-dimentaional data in the format of Figure 2 for each of the following situations?
1) Logistic regression can find a decision boundary correctly, whereas Naive Bayes cannot.
2) Naive Bayes can find a decision boundary correctly, whereas logistic regression cannot.