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'''3. Error data'''<br /> | '''3. Error data'''<br /> | ||
Definition: | Definition: | ||
''True error rate'' of a classifier(h) is defined as the probability that the prediction of Y from X do not exactly equal to Y, namely, <math>\, L(h)=P(h(X)\ | ''True error rate'' of a classifier(h) is defined as the probability that the prediction of Y from X do not exactly equal to Y, namely, <math>\, L(h)=P(h(X) \neq Y)</math>. | ||
''Empirical error rate(training error rate)'' of a classifier(h) is | ''Empirical error rate(training error rate)'' of a classifier(h) is | ||
'''4. Bayes Classifier'''<br /> | '''4. Bayes Classifier'''<br /> | ||
Revision as of 15:37, 30 September 2009
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Course Note for Sept.30th (Classfication_by Liang Jiaxi)
1.
2. Classification
Classification is a function between two random varialbe
3. Error data
Definition:
True error rate of a classifier(h) is defined as the probability that the prediction of Y from X do not exactly equal to Y, namely, [math]\displaystyle{ \, L(h)=P(h(X) \neq Y) }[/math].
Empirical error rate(training error rate) of a classifier(h) is
4. Bayes Classifier