Summary: ETHEM ALPAYDIN
© The MIT Press, 2010
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml2e
Lecture Slides for
Introduction
Questions:
Assessment of the expected error of a learning algorithm: Is
the error rate of 1-NN less than 2%?
Comparing the expected errors of two algorithms: Is k-NN
more accurate than MLP ?
Training/validation/test sets
Resampling methods: K-fold cross-validation
3Lecture Notes for E Alpaydin 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)
Algorithm Preference
Criteria (Application-dependent):
Misclassification error, or risk (loss functions)
Training time/space complexity
Testing time/space complexity
Interpretability

Source: Alpaydýn, Ethem - Department of Computer Engineering, Bogaziçi University

Collections: Computer Technologies and Information Sciences