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048 Outlook attribute has the highest gain, therefore it is used as the decision attribute in the root node. Since Outlook has three possible values, the root node has three branches (sunny, overcast, rain). ” Since we have used Outlook at the root, we only decide on the remaining three attributes: Humidity, Temperature, or Wind. 970 Outlook Sunny Overcast Humidity High No Rain Yes Wind Strong Normal Yes Weak No Fig. 019 Humidity has the highest gain; therefore, it is used as the decision node.
In the decision tree at each node should be associated the non-goal attribute which is most informative among the attributes not yet considered in the path from the root. Entropy is used to measure how informative is a node. Which attribute is the best classifier? The estimation criterion in the decision tree algorithm is the selection of an attribute to test at each decision node in the tree. The goal is to select the attribute that is most useful for classifying examples. A good quantitative measure of the worth of an attribute is a statistical property called information gain that measures how well a given attribute separates the training examples according to their target classification.
The algorithm consists of five steps. 1. T ← the whole training set. Create a T node. 2. If all examples in T are positive, create a ‘P’ node with T as its parent and stop. 3. If all examples in T are negative, create a ‘N’ node with T as its parent and stop. Data Mining Techniques | 25 4. Select an attribute TN according their and X = vi as the 5. For each Ti do: T X with values v1, v2, …, vN and partition T into subsets T1, T2, …, values on X. , N) with T as their parent label of the branch from T to Ti.