<p>Scikit-learn includes quite a few methods for feature ranking, among them:</p>

<ul>
<li>Univariate feature selection (<a href="http://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html" rel="noreferrer">http://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html</a>)</li>
<li>Recursive feature elimination (<a href="http://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html" rel="noreferrer">http://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html</a>)</li>
<li>Randomized Logistic Regression/stability selection (<a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RandomizedLogisticRegression.html" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RandomizedLogisticRegression.html</a>)</li>
</ul>

<p>(see more at <a href="http://scikit-learn.org/stable/modules/feature_selection.html" rel="noreferrer">http://scikit-learn.org/stable/modules/feature_selection.html</a>)</p>

<p>Among those, I definitely recommend giving Randomized Logistic Regression a shot. In my experience, it consistently outperforms other methods and is very stable. 
Paper on this: <a href="http://arxiv.org/pdf/0809.2932v2.pdf" rel="noreferrer">http://arxiv.org/pdf/0809.2932v2.pdf</a></p>

<p><strong>Edit:</strong>
I have written a series of blog posts on different feature selection methods and their pros and cons, which are probably useful for answering this question in more detail:</p>

<ul>
<li><a href="http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/" rel="noreferrer">http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/</a></li>
<li><a href="http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/" rel="noreferrer">http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/</a></li>
<li><a href="http://blog.datadive.net/selecting-good-features-part-iii-random-forests/" rel="noreferrer">http://blog.datadive.net/selecting-good-features-part-iii-random-forests/</a></li>
<li><a href="http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/" rel="noreferrer">http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/</a></li>
</ul>
find important features for classification
<p>I'm trying to classify some EEG data using a logistic regression model (this seems to give the best classification of my data). The data I have is from a multichannel EEG setup so in essence I have a matrix of 63 x 116 x 50 (that is channels x time points x number of trials (there are two trial types of 50), I have reshaped this to a long vector, one for each trial.</p>

<p>What I would like to do is after the classification to see which features were the most useful in classifying the trials. How can I do that and is it possible to test the significance of these features? e.g. to say that the classification was drive mainly by N-features and these are feature x to z. So I could for instance say that channel 10 at time point 90-95 was significant or important for the classification.</p>

<p>So is this possible or am I asking the wrong question?</p>

<p>any comments or paper references are much appreciated. </p>