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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
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
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This note was uploaded on 07/29/2011 for the course STAT 202 at Stanford.