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Unformatted text preview: DATA MINING Susan Holmes © Stats202 Lecture 15 Fall 2010 ABabcdfghiejkl . . . . . . Special Announcements All requests should be sent to [email protected] Homework, the deadline is Tuesday 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 file name (at the end). Midterm, you can bring a one page cheatsheet, no cellphones, no laptops. . . . . . . Last Time:Alternative Classification Methods Rule Based. Instance Based Methods and Nearest Neighbors (knn). Today: Discriminant Analysis: for continuous explanatory variables only. . . . . . . Discrimination for Continuous Explanatory Variables Discriminant functions are the essence of the output from a discriminant analysis. Discriminant functions are the linear combinations of the standardised independent variables which yield the biggest mean differences between the groups. If the response is a dichotomy(only two classes to be predicted) there is one discriminant function; if the reponse variable has k levels(ie there are k classes to predict), up to k-1 discriminant functions can be extracted, and we can test how many are worth extracting. . . . . . . Discriminant Functions Successive discriminant functions are orthogonal to one another, like principal components, but they are not the same as the principal components you would obtain if you just did a principal components analysis on the independent variables, because they are constructed to maximise the differences between the values of the response, not the total variance, but the variance between classes. The initial input data do not...
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This note was uploaded on 07/29/2011 for the course STAT 202 at Stanford.

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