Measuring the performance of classification algorithm
Posted
by Silver Dragon
on Stack Overflow
See other posts from Stack Overflow
or by Silver Dragon
Published on 2009-01-02T11:09:53Z
Indexed on
2010/05/22
22:20 UTC
Read the original article
Hit count: 245
I've got a classification problem in my hand, which I'd like to address with a machine learning algorithm ( Bayes, or Markovian probably, the question is independent on the classifier to be used). Given a number of training instances, I'm looking for a way to measure the performance of an implemented classificator, with taking data overfitting problem into account.
That is: given N[1..100] training samples, if I run the training algorithm on every one of the samples, and use this very same samples to measure fitness, it might stuck into a data overfitting problem -the classifier will know the exact answers for the training instances, without having much predictive power, rendering the fitness results useless.
An obvious solution would be seperating the hand-tagged samples into training, and test samples; and I'd like to learn about methods selecting the statistically significant samples for training.
White papers, book pointers, and PDFs much appreciated!
© Stack Overflow or respective owner