Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices

Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices

Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore key considerations...

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Bibliographic Details
Main Authors:	Sarah Farhadpour, Timothy A. Warner, Aaron E. Maxwell
Format:	Article
Language:	English
Published:	MDPI AG 2024-01-01
Series:	Remote Sensing
Subjects:	deep learning accuracy assessment loss metrics data imbalance class-based statistics
Online Access:	https://www.mdpi.com/2072-4292/16/3/533

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