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A machine learning question

ZraxIA
By ZraxIA
in Programming
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Go to solution Solved by igormp,

Both are rule-based systems, but with the decision tree you have it more "automated" since you can see which variables bring you the most value. As an example, with ID3 you'd have the information gain and entropy for each attribute, which would be calculated based on your available dataset, whereas with a regular, old-style rule-based system you'd do so by hand.

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figured i would find someone hear who has some experience, what algorithm is more optimal, rule based extraction, or decision tree induction based on runtime, space needed ,etc

 

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Both are rule-based systems, but with the decision tree you have it more "automated" since you can see which variables bring you the most value. As an example, with ID3 you'd have the information gain and entropy for each attribute, which would be calculated based on your available dataset, whereas with a regular, old-style rule-based system you'd do so by hand.

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