animal | mammal? | can fly? | carnivore? |
---|---|---|---|
bat | yes | yes | yes |
cow | yes | no | no |
eagle | no | yes | yes |
dog | yes | no | yes |
On paper, show the steps performed by the ID3 algorithm in building a decision tree
that classifies these examples. That is, show the calculations used to determine
which properties to split on, in the appropriate order. When you have completed
your tree on paper, enter the rules into the appropriate file formats and use
the Decision Tree Applet to verify your tree. Recall: you will need to set the splitting
function to Gain before running the algorithm.
One technique commonly used to avoid overspecialization is to divide the examples
into two sets: a training set used to construct the decision tree and a testing set
to subsequently test the constructed tree. The Decision Tree Applet allows you
to specify the size of testing set and will then randomly select the examples.
Reconstruct a decision tree using a testing set whose size is 50% of the total examples.
Do you obtain the same tree?