Page 354 - Computational Statistics Handbook with MATLAB

Chapter 9: Statistical Pattern Recognition 343

ROC Curve
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P( CC ω 0.6

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P(FA)

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F FI F F U URE G 9. RE RE RE 9. 9. 9. 9 9
This shows the ROC curve for Example 9.8.
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MATLAB Functions for Working with Classification Trees
Purpose MATLAB Function
Grows the initial large tree csgrowc
Gets a sequence of minimal complexity trees csprunec
Returns the class for a set of features, using cstreec
the decision tree
Plots a tree csplotreec
Given a sequence of subtrees and an index for cspicktreec
the best tree, extract the tree (also cleans out
the tree)
we will be concerned only with the case where all features are continuous
random variables. The interested reader is referred to Breiman, et al. [1984],
Webb [1999], and Duda, Hart and Stork [2001] for more information on the
other cases.

© 2002 by Chapman & Hall/CRC