<div dir="ltr">Congrats! These look great, thanks to both the authors and the scikit-learn-contrib organizers for putting this together.<div><br></div><div>Nelson</div></div><br><div class="gmail_quote"><div dir="ltr">On Tue, Jul 19, 2016 at 9:09 AM Mathieu Blondel <<a href="mailto:mathieu@mblondel.org">mathieu@mblondel.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div><div><div>Hi everyone,<br><br></div>We are pleased to announce that three new projects recently joined scikit-learn-contrib!<br><br>* imbalanced-learn: <a href="https://github.com/scikit-learn-contrib/imbalanced-learn" target="_blank">https://github.com/scikit-learn-contrib/imbalanced-learn</a><br><br>Python module to perform under sampling and over sampling with various techniques.<br><br>* polylearn: <a href="https://github.com/scikit-learn-contrib/polylearn" target="_blank">https://github.com/scikit-learn-contrib/polylearn</a><br><br>Factorization machines and polynomial networks for classification and regression in Python.<br><br>* forest-confidence-interval: <a href="https://github.com/scikit-learn-contrib/forest-confidence-interval" target="_blank">https://github.com/scikit-learn-contrib/forest-confidence-interval</a><br><br>Confidence intervals for scikit-learn forest algorithms.<br><br></div>We thank the respective authors for their neat contribution to the scikit-learn ecosystem!<br><br></div>Cheers,<br></div>Mathieu<br></div>
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