Chapter 2， Page 23

[FZhg]

“Life is more complicated for the “full” decision tree. Certainly
if it is given a test example that is identical to one of the training
examples, it will do the right thing (assuming no noise). But for
everything else, it will only get about 50% error. This means that
even if every other test point happens to be identical to one of the
training points, it would only get about 25% error. In practice, this is
probably optimistic, and maybe only one in every 10 examples would
match a training example, yielding a 35% error”

The 35% error rate should be 45% error.
Since we have 10 test example, one of which must be right since it matches the training example.
The rest of the 9 examples are just random guess. Then the expected examples that we have got right are 4.5 + 1 = 5.5.
Thus the expected error rate should be 45%. Unless you take into account the condition that the number of the examples have to be intergers instead of decimals.

[meeraray]

I had this exact issue too! Unless there's something I'm missing, I think it's a typo.