Considering the example in Table 5, what is the maximal number of leaf (decision) nodes that we can have in a decision tree for this data? What is the maximal possible depth of a decision tree for this data? Draw the tree, explain which attribute should be the root node and why?

Table 5 Training Set Example Instance Classification 1 2 3 4 5 6 C1 C1 C2 C1 C2 C2 Attribute 1 Y Y Y N N N Attribute 2 Y Y N N Y Y