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Lesson 35 - Module 12 Machine Learning Version 1 CSE IIT...

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Module 12 Machine Learning Version 1 CSE IIT, Kharagpur
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Lesson 35 Rule Induction and Decision Tree - I Version 1 CSE IIT, Kharagpur
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12.3 Decision Trees Decision trees are a class of learning models that are more robust to noise as well as more powerful as compared to concept learning. Consider the problem of classifying a star based on some astronomical measurements. It can naturally be represented by the following set of decisions on each measurement arranged in a tree like fashion. L L u u m m i i n n o o s s i i t t y y M M a a s s s s T T y y p p e e A A T T y y p p e e B B T T y y p p e e C C > > T T 1 1 < < = = T T 1 1 > > T T 2 2 < < = = T T 2 2 12.3.1 Decision Tree: Definition A decision-tree learning algorithm approximates a target concept using a tree representation, where each internal node corresponds to an attribute, and every terminal node corresponds to a class. There are two types of nodes: o Internal node.- Splits into different branches according to the different values the corresponding attribute can take. Example: luminosity <= T1 or
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