Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

ABSTRACT: Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for perfo

SUBMITTER: Levman J 

PROVIDER: S-EPMC10093437 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature