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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis

Fig. 2

Workflow for Model Selection and Parameter Fitting. (a) The diagram shows how training data is used to train and evaluate multiple hypotheses. The model that gives the best classification accuracy on the exhaustive 1-holdout cross-validation constitutes the lead hypothesis. (b) The diagram shows how data is used to train and test the lead hypothesis. The best performing model is refitted to all the samples in the training data, and then used to score samples from the validation data set

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BMC Bioinformatics

ISSN: 1471-2105

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