A classification tree is manually built on the data below to predict whether or not a student will pass a course. The predictor variables include the categorisation of 2nd year marks (categorised as high (H) / medium (M) / or low (L)) and whether or not the majority of lectures were attended (yes (Y) or no (N)). The response is coded as a pass (P) or fail (F).

2nd yr marks	Attended	Passed
L	N	F
L	Y	P
M	N	F
M	Y	P
H	N	P
H	Y	P

You enter the data into R, fit a classification tree using the tree package with default values, and get output where no splits have occurred. Why do you think the tree output is not showing any splits?

A. None of the splits would result in an improvement. B. The improvement in fit from any of the splits is not sufficiently large. C. impossible to say D. There are not enough observations in the root node for a split to occur