Page 81 - Data Science Algorithms in a Week

Decision Trees

└── [Wind=Breeze]
└── [Play=Yes]

Classification:
Now that we have constructed the decision tree, we would like to use it to classify a data
sample (warm,strong,sunny,?) into one of the two classes in the set {no,yes}.

We start at the root. What value does the attribute temperature have in that instance?
Warm, so we go to the middle branch. What value does the attribute wind have in that
instance? Strong, so the instance would fall into the class no since we have arrived already
in the leaf node.

So, our friend would not want to play chess with us in the park according to the decision
tree classification algorithm. Please note that the Naive Bayes' algorithm stated otherwise.
An understanding of the problem is required to choose the best possible method. At other
times, a method with a greater accuracy is the one that takes into consideration results of
several algorithms or several classifiers, as in the case of random forest algorithm in the next
chapter.

Going shopping - dealing with data

inconsistency

We have the following data about the shopping preferences of our friend, Jane:

Temperature Rain Shopping

Cold None Yes
Warm None No
Cold Strong Yes

Cold None No
Warm Strong No

Warm None Yes
Cold None ?

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