WittenFrankChapter4Part4Prism (2)

WittenFrankChapter4Part4Prism (2) - Data Mining Algorithms...

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Data Mining – Algorithms: Prism – Learning Rules via Separating and Covering Chapter 4, Section 4.4
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Rules Can be directly read off a decision tree – but those might not be the most compact or effective rules Common approach – take each class in turn and find a way of “covering” all instances in it, while excluding instances not in the class
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Let’s use My Weather Data Again Again, Let’s take this a little more realistic than book does Divide into training and test data Let’s save the last record as a test (using my weather, nominal … and assuming we’re working on the play?=yes class first … We’re looking for a rule in the form if ___ Then play? = yes Possible ways of filling include: Outlook = sunny Outlook = overcast Temperature = hot
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Find the best filler using training data We look at proportion of instances that match the left hand side that also match the right hand side LHS Matches LHS Of those, Match RHS Ratio Outlook = sunny 5 4 .80 Outlook = Overcast 4 2 .50 Outlook = Rainy 4 0 .00 Temp = Hot 4 1 .25 Temp = Mild 5 3 .60 Temp = Cool 4 2 .50 Humid = High 6 3 .50 Humid = Normal 7 3 .43 Windy = TRUE 5 4 .80 Windy = False 8 2 .25
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Refining Rule If this rule is not accurate enough for us (based on a threshold), we’re going to try to refine it by adding a clause(s) Now, we’re looking to fill in a clause in the following: if outlook = sunny and _____ then play? = yes We consider the accuracy of all possible ways of filling this blank …
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Find the best filler using training data We look at proportion of instances that match the left hand side that also match the right hand side LHS Matches LHS Of those, Match RHS Ratio Outlook = Sunny & Temp = Hot 2 1 .50 Outlook = Sunny & Temp = Mild 2 2 1.00 Outlook = Sunny & Temp = Cool 1 1 1.00 Outlook = Sunny & Humid = High 3 2 .67 Outlook = Sunny & Humid = Normal 2 2 1.00 Outlook = Sunny & Windy = TRUE 2 2 1.00 Outlook = Sunny & Windy = False 3 2 .67
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Still more to cover though This rule only covers 2 of the 6 play=yes days This approach looks more for pockets of a success whereas ID3 is looking more at the big picture So we temporarily toss those 2 instances and work on another rule
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Example: My Weather (Nominal) Outlook Temp Humid Windy Play? sunny hot high FALSE no sunny hot high TRUE yes overcast hot high FALSE no rainy mild high FALSE no rainy cool normal FALSE no rainy cool normal TRUE no overcast cool normal TRUE yes sunny cool normal FALSE yes rainy mild normal FALSE no overcast mild high TRUE yes overcast hot normal FALSE no rainy mild high TRUE no TEST
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We’re Looking for another rule … in the form if ___ Then play? = yes Again, possible ways of filling include: Outlook = sunny Outlook = overcast Temperature = hot However, our data is a little different now
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Find the best filler using training data We look at proportion of instances that match the left hand side that also match the right hand side LHS Matches LHS Of those, Match RHS Ratio Outlook = sunny 3 2 .67 Outlook = Overcast 4 2 .50 Outlook = Rainy 4 0 .00
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