Levels of Confidence and Utility for Binary Classifiers
Two performance measures for binary tree classifiers are introduced: the level of confidence and the level of utility. Both measures are probabilities of desirable events in the construction process of a classifier and hence are easily and intuitively interpretable. The statistical estimation of the...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Stats |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-905X/7/4/71 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Two performance measures for binary tree classifiers are introduced: the level of confidence and the level of utility. Both measures are probabilities of desirable events in the construction process of a classifier and hence are easily and intuitively interpretable. The statistical estimation of these measures is discussed. The usual maximum likelihood estimators are shown to have upward biases, and an entropy-based bias-reducing methodology is proposed. Along the way, the basic question of appropriate sample sizes at tree nodes is considered. |
|---|---|
| ISSN: | 2571-905X |