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<div class="moz-cite-prefix">On 03/19/2017 03:47 PM, Thomas
Evangelidis wrote:<br>
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cite="mid:CAACvdx17Ev3jr0ds2bLyJc0RqZkqJH7Rtx=s1ZaodmUvCkcB8Q@mail.gmail.com"
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style="font-size:large;color:rgb(0,0,0)">Which of the
following methods would you recommend to select good features
(<=50) from a set of 534 features in order to train a
MLPregressor? Please take into account that the datasets I use
for training are small.<br>
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style="font-size:large;color:rgb(0,0,0)"><br>
<a moz-do-not-send="true"
href="http://scikit-learn.org/stable/modules/feature_selection.html">http://scikit-learn.org/stable/modules/feature_selection.html</a><br>
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<div class="gmail_default"
style="font-size:large;color:rgb(0,0,0)">And please don't tell
me to use a neural network that supports the dropout or any
other algorithm for feature elimination. This is not
applicable in my case because I want to know the best 50
features in order to append them to other types of feature
that I am confident that are important.<br>
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</blockquote>
You can always use forward or backward selection as implemented in
mlxtend if you're patient. As your dataset is small that might work.<br>
However, it might be hard tricky to get the MLP to run consistently
- though maybe not...<br>
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