Modeling Decision Tree Performance with the Power Law
Abstract
This paper discusses the use of a power law to predict decision tree performance. Power laws are fit to learning curves of decision trees trained on data sets from the UCI repository. The learning curves are generated by training C4.5 on different size training sets. The power law predicts diminishing returns in terms of error rate as training set size increase. By characterizing the learning curve with a power law, the error rate for a given size training set can be projected. This projection can be used in estimating the amount of data needed to achieve an acceptable error rate, and the cost effectiveness of further data collection.
Cite
Text
Frey and Fisher. "Modeling Decision Tree Performance with the Power Law." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.Markdown
[Frey and Fisher. "Modeling Decision Tree Performance with the Power Law." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.](https://mlanthology.org/aistats/1999/frey1999aistats-modeling/)BibTeX
@inproceedings{frey1999aistats-modeling,
title = {{Modeling Decision Tree Performance with the Power Law}},
author = {Frey, Lewis J. and Fisher, Douglas H.},
booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics},
year = {1999},
volume = {R2},
url = {https://mlanthology.org/aistats/1999/frey1999aistats-modeling/}
}