ohdatamineCART

ohdatamineCART - DATA MINING Susan Holmes Stats202 Lecture...

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. . . . . . DATA MINING Susan Holmes © Stats202 Lecture 12 Fall 2010 ABabcdfghiejkl
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. . . . . . Special Announcements I Homework questions in ofFce hours (6 times a week). I All other requests should be sent to [email protected] . I Homework, the deadline is Monday 5.00pm, all hw not within the deadline is rejected (we have an automatic system). Please don't forget to add your sunet id to your hw Fle name (at the end). I Your grades should be in the coursework system, your graded homeworks will be in dropbox folders today. I Interpretation means talking about the conclusions you draw about the data at hand, not the methodology, saying the Frst map shows a PCA with the most variance is not enough. I Show your R work.
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. . . . . . Last Time: Decision Trees and Classifcation Examples I Two sets oF Data: Training and Test. I Response Y is a nominal/categorical variable. I Explanatory variables can be continuous AND nominal AND ordinal. I Indices oF Purity: Gini, Entropy (Deviance) and Misclassifcation.
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. . . . . . Example of ClassiFcation Trees library(ElemStatLearn) ##For spam data data(spam) ###Last few variables look like this: A.51 A.52 A.53 A.54 A.55 A.56 A.57 spam 1 0 0.778 0.000 0.000 3.756 61 278 spam 2 0 0.372 0.180 0.048 5.114 101 1028 spam 3 0 0.276 0.184 0.010 9.821 485 2259 spam 4 0 0.137 0.000 0.000 3.537 40 191 spam > nrow(spam) [1] 4601 > sum(spam$spam!="email")/nrow(spam) [1] 0.3940448 > sum(spam$spam=="email")/nrow(spam) [1] 0.6059552
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. . . . . . Example of ClassiFcation Trees
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This note was uploaded on 07/29/2011 for the course STAT 202 at Stanford.

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ohdatamineCART - DATA MINING Susan Holmes Stats202 Lecture...

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