<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
</head>
<body text="#000000" bgcolor="#FFFFFF">
I think you can also use RFECV directly without doing any wrapping.<br>
<br>
<br>
<div class="moz-cite-prefix">On 11/20/19 12:24 AM, Brown J.B. via
scikit-learn wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CAJe_vxCGs4BBBOGOh=Ri1cxRvjuCR=WOcgwNRSC3h5tjbfBzZA@mail.gmail.com">
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<div dir="ltr">
<div dir="ltr">
<div>Dear Malik,</div>
<div><br>
</div>
<div>Your request to do performance checking of the steps of
SVM-RFE is a pretty common task.</div>
<div><br>
</div>
<div>Since the contributors to scikit-learn have done great to
make the interface to RFE easy to use, the only real work
required from you would be to build a small wrapper function
that:</div>
<div>(a) computes the step sizes you want to output prediction
performances for, and<br>
</div>
<div>(b) loops over the step sizes, making each step size the
n_features attribute of RFE (and built from the remaining
features), making predictions from a SVM retrained (and
possibly optimized) on the reduced feature set, and then
outputting your metric(s) appropriate to your problem.</div>
<div><br>
</div>
<div>Tracing the feature weights is then done by accessing the
"coef_" attribute of the linear SVM trained.</div>
<div>This can be output in loop step (b) as well.<br>
</div>
<div><br>
</div>
</div>
<div class="gmail_quote">
<blockquote class="gmail_quote" style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
<div dir="ltr">where each time 10% for the features are
removed.
<div>How one can get the accuracy overall the levels of
the elimination stages. For example, I want to get
performance over 1000 features, 900 features, 800
features,....,2 features, 1 feature.</div>
</div>
</blockquote>
<div><br>
</div>
<div>Just a technicality, but by 10% reduction you would have</div>
<div>1000, 900, 810, 729, 656, ... .</div>
<div>Either way, if you allow your wrapper function to take a
pre-computed list of feature sizes, you can flexibly change
between a systematic way or a context-informed way of
specifying feature sizes (and resulting weights) to trace.</div>
<div><br>
</div>
<div>Hope this helps.</div>
<div><br>
</div>
<div>J.B. Brown</div>
<div>Kyoto University Graduate School of Medicine<br>
</div>
<div><br>
</div>
</div>
</div>
<br>
<fieldset class="mimeAttachmentHeader"></fieldset>
<pre class="moz-quote-pre" wrap="">_______________________________________________
scikit-learn mailing list
<a class="moz-txt-link-abbreviated" href="mailto:scikit-learn@python.org">scikit-learn@python.org</a>
<a class="moz-txt-link-freetext" href="https://mail.python.org/mailman/listinfo/scikit-learn">https://mail.python.org/mailman/listinfo/scikit-learn</a>
</pre>
</blockquote>
<br>
</body>
</html>