Decision tree learninga describe the main steps in the

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Decision Tree Learning

(a) Describe the main steps in the basic decision tree learning algorithm. The table below contains a sample S of ten examples. Each example is described using two Boolean attributes A and B. Each is labelled (classied) by the target Boolean function.

1559_Decision Tree Learning.png

(b) What is the entropy of thse examples with respect to the given classication ?

[Note: you must show how you got your answer using the standard formula.] This table gives approximate values of entropy for frequencies of positive examples in a two-class sample.

1979_Decision Tree Learning1.png

(d) What is the information gain of attribute B on sample S above ?

(e) Which would be chosen as the \best" attribute by a decision tree learner using the information gain splitting criterion ? Why ?

(f) Describe a method for overtting-avoidance in decision tree learning.

Reference no: EM13380328

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