Skip main navigation

Performance optimization

Ian Witten shows several "wrapper" metalearners for that optimize parameters for best performance. Avoid doing this manually: you're bound to overfit!

Machine learning methods often involve several parameters, which should be optimized for best performance. Optimizing them manually is tedious, and also dangerous, because you risk overfitting the data (unless you hold out some data for final testing). Weka contains three “wrapper” metalearners that optimize parameters for best performance using internal cross-validation. CVParameterSelection selects the best value for a parameter; GridSearch optimizes two parameters by searching a 2D grid; and the ThresholdSelector selects a probability threshold.

This article is from the free online

More Data Mining with Weka

Created by
FutureLearn - Learning For Life

Reach your personal and professional goals

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates.

Join over 18 million learners to launch, switch or build upon your career, all at your own pace, across a wide range of topic areas.

Start Learning now