A Decision Tree Is Built to Determine Individuals Likely to Default

A decision tree is built to determine individuals likely to default on an unsecured loan. The null hypothesis states that an individual will not default on the loan. The decision tree correctly classifies 80% of the instances in a test dataset. Fifteen percent of the mistakes made by the model are type 1 errors. What can be said about the performance of the model?

A) The accuracy of the model for correctly determining those individuals who did not default on their loan was at least 75%.
B) The accuracy of the model for correctly determining those individuals who defaulted on their loan was at least 75%.
C) The majority of errors made by the model accepted individuals who defaulted.
D) The majority of errors made by the model rejected individuals who did not default.
E) More than one of a,b,c or d is correct.