An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5001642?pdf=render |