themis

themis-package

<img src="figures/logo.png?package=themis&version=1.1.0" style="float: right" alt="logo" width="120">
A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 tools:::Rd_expr_doi("10.48550/arXiv.1106.1813"), BorderlineSMOTE 2005 tools:::Rd_expr_doi("10.1007/11538059_91") and ADASYN 2008. Or by decreasing the number of majority cases using NearMiss 2003 <a href="https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf">https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf</a> or Tomek link removal 1976.

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A dataset with an uneven number of cases in each class is
said to be unbalanced. Many models produce a subpar performance on
unbalanced datasets. A dataset can be balanced by increasing the
number of minority cases using SMOTE 2011
<doi:10.48550/arXiv.1106.1813>, BorderlineSMOTE 2005
<doi:10.1007/11538059_91> and ADASYN 2008. Or by decreasing the number
of majority cases using NearMiss 2003
<https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
link removal 1976.

Emil Hvitfeldt

Extra Recipes Steps for Dealing with Unbalanced Data

Posit Software, PBC 

themis-package function

Maintainer: Emil Hvitfeldt <a href="/link/emil.hvitfeldt%40posit.co?package=themis&version=1.1.0" data-mini-rdoc="themis::emil.hvitfeldt@posit.co">emil.hvitfeldt@posit.co</a> (<a href="https://orcid.org/0000-0002-0679-1945">ORCID</a>)
Authors:<ul>
<li>Emil Hvitfeldt <a href="/link/emil.hvitfeldt%40posit.co?package=themis&version=1.1.0" data-mini-rdoc="themis::emil.hvitfeldt@posit.co">emil.hvitfeldt@posit.co</a> (<a href="https://orcid.org/0000-0002-0679-1945">ORCID</a>)</li>
</ul>Other contributors:<ul>
<li>Posit Software, PBC (<a href="https://ror.org/03wc8by49">ROR</a>) [copyright holder, funder]</li>
</ul>

Author

<img src='figures/logo.png' style='float: right' alt='logo' width='120' />
A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 tools:::Rd_expr_doi("10.48550/arXiv.1106.1813"), BorderlineSMOTE 2005 tools:::Rd_expr_doi("10.1007/11538059_91") and ADASYN 2008. Or by decreasing the number of majority cases using NearMiss 2003 <a href='https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf'>https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf</a> or Tomek link removal 1976.

themis: Extra Recipes Steps for Dealing with Unbalanced Data — themis-package

Maintainer: Emil Hvitfeldt <a href='mailto:emil.hvitfeldt@posit.co'>emil.hvitfeldt@posit.co</a> (<a href='https://orcid.org/0000-0002-0679-1945'>ORCID</a>)
Authors:<ul>
<li>Emil Hvitfeldt <a href='mailto:emil.hvitfeldt@posit.co'>emil.hvitfeldt@posit.co</a> (<a href='https://orcid.org/0000-0002-0679-1945'>ORCID</a>)</li>
</ul>

Other contributors:<ul>
<li>Posit Software, PBC (<a href='https://ror.org/03wc8by49'>ROR</a>) [copyright holder, funder]</li>
</ul>

themis-package: themis: Extra Recipes Steps for Dealing with Unbalanced Data

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