Classifying with a decision tree

Once we have constructed a decision tree from the data with the attributes A₁, ..., A_m and the classes {c₁, ..., c_k}, we can use this decision tree to classify a new data item with the attributes A₁, ..., A_m into one of the classes {c₁, ..., c_k}.

Given a new data item that we would like to classify, we can think of each node, including the root, as a question for the data sample: What value does that data sample have for the selected attribute, A_i? Then, based on the answer, we select a branch of the decision tree and move on to the next node. Then, another question is answered about the data sample, and another, until the data sample reaches the leaf node. A leaf node has one of the classes {c₁, ..., c_k} associated with it; for example, c_i. Then, the decision tree algorithm would classify the data sample into the class, c_i.

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