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          <dc:title>&lt;p&gt;Combined out-of-fold predictions, part 2.&lt;/p&gt;</dc:title>
          <dc:creator>Raed M. Ennab (17251966)</dc:creator>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Medicine</dc:subject>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Mathematical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Marine Biology</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>Infectious Diseases</dc:subject>
          <dc:subject>Virology</dc:subject>
          <dc:subject>large synthetic heart</dc:subject>
          <dc:subject>fitted within outer</dc:subject>
          <dc:subject>controlled benchmark rather</dc:subject>
          <dc:subject>train &amp;# 8211</dc:subject>
          <dc:subject>000017 &amp;# 8211</dc:subject>
          <dc:subject>row test set</dc:subject>
          <dc:subject>controlled validation framework</dc:subject>
          <dc:subject>cannot establish performance</dc:subject>
          <dc:subject>calibration intercepts ranged</dc:subject>
          <dc:subject>fold outer splits</dc:subject>
          <dc:subject>duplicated feature profiles</dc:subject>
          <dc:subject>marginally higher auroc</dc:subject>
          <dc:subject>regularized logistic regression</dc:subject>
          <dc:subject>test feature</dc:subject>
          <dc:subject>&amp;# 8722</dc:subject>
          <dc:subject>validation optimism</dc:subject>
          <dc:subject>external performance</dc:subject>
          <dc:subject>duplicated identifiers</dc:subject>
          <dc:subject>calibration intercept</dc:subject>
          <dc:subject>regression preprocessing</dc:subject>
          <dc:subject>logistic regression</dc:subject>
          <dc:subject>xlink "&gt;</dc:subject>
          <dc:subject>training data</dc:subject>
          <dc:subject>secondary measures</dc:subject>
          <dc:subject>reproducible benchmarking</dc:subject>
          <dc:subject>principal contribution</dc:subject>
          <dc:subject>primary measure</dc:subject>
          <dc:subject>outcome prevalence</dc:subject>
          <dc:subject>outcome labels</dc:subject>
          <dc:subject>one leakage</dc:subject>
          <dc:subject>novel algorithm</dc:subject>
          <dc:subject>mean optimism</dc:subject>
          <dc:subject>learning classifiers</dc:subject>
          <dc:subject>fully rerunnable</dc:subject>
          <dc:subject>four machine</dc:subject>
          <dc:subject>fold variability</dc:subject>
          <dc:subject>clinical populations</dc:subject>
          <dc:subject>brier scores</dc:subject>
          <dc:subject>brier score</dc:subject>
          <dc:subject>boosting models</dc:subject>
          <dc:subject>average precision</dc:subject>
          <dc:subject>13 predictors</dc:subject>
          <dc:description>&lt;p&gt;Gzip-compressed CSV containing the second half of the 630,000 training observations, outcomes, saved outer folds, and predictions from all four models.&lt;/p&gt; &lt;p&gt;(GZ)&lt;/p&gt;</dc:description>
          <dc:date>2026-09-21T17:51:24Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pone.0354304.s006</dc:identifier>
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