Efficiency of different measures for defining the applicability domain of classification models

Efficiency of different measures for defining the applicability domain of classification models

Abstract The goal of defining an applicability domain for a predictive classification model is to identify the region in chemical space where the model’s predictions are reliable. The boundary of the applicability domain is defined with the help of a measure that shall reflect the reliability of an...

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Bibliographic Details
Main Authors: Waldemar Klingspohn, Miriam Mathea, Antonius ter Laak, Nikolaus Heinrich, Knut Baumann
Format: Article
Language:English
Published: BMC 2017-08-01
Series:Journal of Cheminformatics
Subjects:
Applicability domain
Applicability domain measures
Reject option
Novelty detection
Confidence estimation
Class probability estimation
Online Access:http://link.springer.com/article/10.1186/s13321-017-0230-2

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http://link.springer.com/article/10.1186/s13321-017-0230-2

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