Entropy using Decision Tree's

Question

This is a question in a previous exam paper that I have been asked to look at; the slide we have used to learn do not make sense of this. I understand how to use a table to get the Entropy, but not how to use the equation provided to do so

Question

Train a decision tree on the data represented by attributes A1, A2, A3 and outcome C described below:

A1 A2 A3 C
1  0  1  0
0  1  1  1
0  0  1  0

For log₂(1/3) = 1.6 and log₂(2/3) = 0.6, answer the following questions:

a) What is the value of entropy H for the given set of training example?

b) What is the portion of the positive samples split by attribute A2?

c) What is the value of information gain, G(A2), of attribute A2?

d) What is IFTHEN rule(s) for the decision tree?

Answer 1

I guess that part b will be 3. As if you added 1 to A2 on Row 1 or 3, C would become 1.

For Row 2, if you removed 1 from A2, then C would become 0.