DSCI4520_TreeAlgorithms_4

DSCI4520_TreeAlgorithms_4 - DSCI 4520/5240 DATA MINING DSCI...

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Lecture 4 - 1 DSCI 4520/5240 DATA MINING Some slide material taken from: Witten & Frank 2000, Olson & Shi 2007, de Ville 2006, SAS Education 2005 DSCI 4520/5240 Lecture 4 Decision Tree Algorithms DSCI 4520/5240 DBDSS (DATA MINING)
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Lecture 4 - 2 DSCI 4520/5240 DATA MINING Objective Review of some Decision Tree algorithms.
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Lecture 4 - 3 DSCI 4520/5240 DATA MINING This example is related to determining credit risks. We have a total of 10 people. 6 are good risks and 4 are bad. We apply splits to the tree based on employment status. When we break this down, we find that there are 7 employed and 3 not employed. Of the 3 that are not employed , all of them are bad credit risks and thus we have learned something about our data. Decision Trees: Credit risk example Note that here we cannot split this node down any further since all of our data is grouped into one set. This is called a pure node . The other node, however, can be split again based on a different criterion. So we can continue to grow the tree on the left hand side. CORRESPONDING RULES: IF employed = yes AND married = yes THEN risk = good IF employed = yes AND married = no THEN risk = good IF employed = no THEN risk = bad
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Lecture 4 - 4 DSCI 4520/5240 DATA MINING Decision Tree performance Confidence is the degree of accuracy of a rule. Support is the degree to which the rule conditions occur in the data. EXAMPLE: if 10 customers purchased Zane Grey’s The Young Pitcher and 8 of them also purchased The Short Stop , the rule: {IF basket has The Young Pitcher THEN basket has The Short Stop } has confidence of 0.80. If these purchases were the only 10 to cover these books out of 10,000,000 purchases, the support is only 0.000001.
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Lecture 4 - 5 DSCI 4520/5240 DATA MINING Rule Interestingness Interestingness is the idea that Data Mining discovers something unexpected. Consider the rule: {IF basket has eggs THEN basket has bacon }. Suppose the confidence level is 0.90 and the support level is 0.20. This may be a useful rule, but it may not be interesting if the grocer was already aware of this association. Recall the definition of DM as the discovery of previously unknown knowledge!
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Lecture 4 - 6 DSCI 4520/5240 DATA MINING Rule Induction algorithms 1R ID3 C4.5/C5.0 CART CHAID CN2 BruteDL SDL They are recursive algorithms that identify data partitions of progressive separation with respect to the outcome . The partitions are then organized into a decision tree . Common Algorithms:
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Lecture 4 - 7 DSCI 4520/5240 DATA MINING Illustration of two Tree algorithms 1R and Discretization in 1R Naïve Bayes Classification ID3: Min Entropy and Max Info Gain
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Lecture 4 - 8 DSCI 4520/5240 DATA MINING 1R
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Lecture 4 - 9 DSCI 4520/5240 DATA MINING 1R: Inferring Rudimentary Rules 1R: learns a 1-level decision tree In other words, generates a set of rules that all test on one particular attribute Basic version (assuming nominal attributes) One branch for each of the attribute’s values
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DSCI4520_TreeAlgorithms_4 - DSCI 4520/5240 DATA MINING DSCI...

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