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Can can get the coefficients on the scaled data with<br>
pipeline_lr.named_steps_['clf'].coef_<br>
though<br>
<br>
<br>
<div class="moz-cite-prefix">On 08/28/2017 12:08 AM, Raga Markely
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CAOLKFqsiSvhK0S0KGK_BYJZGHe5Wi8U7Tsf6zhh+8FJrQwkUTg@mail.gmail.com">
<div dir="ltr">No problem, thank you!
<div><br>
</div>
<div>Best,</div>
<div>Raga</div>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Mon, Aug 28, 2017 at 12:01 AM, Joel
Nothman <span dir="ltr"><<a
href="mailto:joel.nothman@gmail.com" target="_blank"
moz-do-not-send="true">joel.nothman@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">
<div>No, we do not have a way to get the coefficients with
respect to the input (pre-scaling) space.</div>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">
<div>
<div class="h5">On 28 August 2017 at 13:20, Raga
Markely <span dir="ltr"><<a
href="mailto:raga.markely@gmail.com"
target="_blank" moz-do-not-send="true">raga.markely@gmail.com</a>></span>
wrote:<br>
</div>
</div>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div>
<div class="h5">
<div dir="ltr">Hello,
<div><br>
</div>
<div>I am wondering if it's possible to get the
weight coefficients of logistic regression
from a pipeline?</div>
<div><br>
</div>
<div>For instance, I have the followings:</div>
<div>
<blockquote class="gmail_quote"
style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">clf_lr =
LogisticRegression(penalty='l1<wbr>', C=0.1)<br>
pipe_lr = Pipeline([['sc',
StandardScaler()], ['clf', clf_lr]])<br>
pipe_lr.fit(X, y)</blockquote>
</div>
<div><br>
</div>
<div>Does pipe_lr have an attribute that I can
call to get the weight coefficient?</div>
<div><br>
</div>
<div>Or do I have to get it from the classifier
as follows?</div>
<blockquote class="gmail_quote"
style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">X_std =
StandardScaler().fit_transform<wbr>(X)<br>
clf_lr = LogisticRegression(penalty='l1<wbr>',
C=0.1)<br>
clf_lr.fit(X_std, y)<br>
clf_lr.coef_</blockquote>
<div><br>
</div>
<div>Thank you,</div>
<div>Raga</div>
<div><br>
</div>
</div>
<br>
</div>
</div>
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