Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier.

ABSTRACT: Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classific

SUBMITTER: Kircher M 

PROVIDER: S-EPMC9956321 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature