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35 lines
938 B
Go
35 lines
938 B
Go
/*
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This package implements decision trees.
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ID3DecisionTree:
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Builds a decision tree using the ID3 algorithm
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by picking the Attribute which maximises
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Information Gain at each node.
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Attributes must be CategoricalAttributes at
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present, so discretise beforehand (see
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filters)
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CART (Classification and Regression Trees):
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Builds a binary decision tree using the CART algorithm
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using a greedy approach to find the best split at each node.
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Can be used for regression and classficiation.
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Attributes have to be FloatAttributes even for classification.
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Hence, convert to Integer Labels before hand for Classficiation.
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RandomTree:
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Builds a decision tree using the ID3 algorithm
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by picking the Attribute amongst those
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randomly selected that maximises Information
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Gain
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Attributes must be CategoricalAttributes at
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present, so discretise beforehand (see
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filters)
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*/
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package trees
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