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    <identifier>10.57760/sciencedb.02425</identifier>
    <datestamp>2022-10-18T21:32:59Z</datestamp>
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  <dc:date>2022-10-18</dc:date>
  <dc:title>Self Consistent Classifier Verification Dataset</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.02425</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Initial SCLC and SCEC algorithm development was performed with the generated data samples. Regression matrix was generated in different combinations with various number of structures and descriptors, and varying error model. The observed values were calculated with generated coefficients and then classified with the selected cutoff.Generation of data was applied to observe the regression coefficients and weights convergence as well as to check with diagnostic plots that the results correspond to those expected and thus perform the verification.&amp;nbsp;This is an example of such dataset</dc:description>
  <dc:subject>structure-activity relationships; self-consistent regression; logistic regression; self-consistent logistic classifier; self-consistent extreme classifier; classification models</dc:subject>
  <dc:creator>Leonid Stolbov</dc:creator>
  <dc:creator>Vladimir Poroikov</dc:creator>
  <dc:creator>Dmitry Filimonov</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/publicdomain/zero/1.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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