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     - DevelopersGuide

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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>

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     <a class="navbar-brand me-2" href="index.html">ClassifyR</a>

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   <main id="main" class="col-md-9"><div class="section level1">

 <div class="page-header"><h1 id="classifyr-performance-evaluation-for-multi-view-data-sets-and-seamless-integration-with-multiassayexperiment-and-bioconductor">ClassifyR: Performance evaluation for multi-view data sets and seamless integration with MultiAssayExperiment and Bioconductor<a class="anchor" aria-label="anchor" href="#classifyr-performance-evaluation-for-multi-view-data-sets-and-seamless-integration-with-multiassayexperiment-and-bioconductor"></a>

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-<p><img src="reference/figures/ClassifyRsticker.png" align="right"></p>

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 <p>ClassifyR’s performance evaluation focuses on model stability and interpretability. Based on repeated cross-validation, it is possible to evaluate feature selection stability and also per-sample prediction accuracy. Also, multiple omics data assays on the same samples are becoming more popular and ClassifyR supports a range of multi-view methods to evaluate which data view is the most predictive and combine data views to evaluate if multiple views provide superior predictive performance to a single data view.</p>

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 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>

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   introduction: introduction.html

   multiViewMethods: multiViewMethods.html

   performanceEvaluation: performanceEvaluation.html

-last_built: 2022-11-09T03:41Z

+last_built: 2022-11-10T22:29Z

 urls:

   reference: https://sydneybiox.github.io/ClassifyR/reference

   article: https://sydneybiox.github.io/ClassifyR/articles

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