Page 77 - Data Science Algorithms in a Week

Decision Trees

Classifying a data sample with the swimming

preference decision tree
Let us construct a decision tree for the swimming preference example with the ID3
algorithm. Consider a data sample (good, cold,?) and we would like to use the constructed
decision tree to decide into which class it should belong.
Start with a data sample at the root of the tree. The first attribute that branches from the root
is swimming suit, so we ask for the value for the attribute swimming suit of the sample
(good, cold,?). We learn that the value of the attribute is swimming suit=good; therefore,
move down the rightmost branch with that value for its data samples. We arrive at the node
with the attribute water temperature and ask the question: what is the value of the attribute
water temperature for the data sample (good, cold,?)? We learn that for that data sample, we
have water temperature=cold; therefore, we move down the left branch into the leaf node.
This leaf is associated with the class swimming preference=no. Therefore, the decision tree
would classify the data sample (good, cold,?) to be in that class swimming preference; that is,
to complete it to the data sample (good, cold, no).

Therefore, the decision tree says that if one has a good swimming suit, but the water
temperature is cold, then one would still not want to swim based on the data collected in
the table.

Playing chess - analysis with decision tree

Let us take an example from the Chapter 2, Naive Bayes again:

Temperature Wind Sunshine Play
Cold Strong Cloudy No

Cold Strong Cloudy No
Warm None Sunny Yes

Hot None Sunny No
Hot Breeze Cloudy Yes
Warm Breeze Sunny Yes

Cold Breeze Cloudy No
Cold None Sunny Yes

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