The coef_ available from LinearSVC will be somewhat indicative of the
relative importance of each feature.
But you might want to look into our feature selection documentation:
http://scikit-learn.org/stable/modules/feature_selection.html
___
also (sorry for spamming the list!) should have said the Allen Institute
has a ton of data:
https://www.nwb.org/allen-cell-types-database/
and check out the cool dataset with this paper:
Hi Jeffrey,
check out these here for neuron data and fmri:
http://crcns.org/
And the ones here for fmri:
https://openfmri.org/
You can get started by installing one of the following packages and using
their dataset downloaders
Hi Jeff,
here's a couple of places to start, I'm sure other people can recommend
more:
https://crcns.org/
https://www.nature.com/sdata/policies/repositories (see under Neuroscience)
There's also the challenge that Gael just announced, predicting autism from
brain imaging data:
Hi guys,
I want to perform some basic data analysis. Anyone have good recommendations
where I can obtain free datasets. I was thinking of trying to do something
related to neuroscience. But, kaggle doesn't have many datasets for this focus.
Thank you,
Jeff Levesque
Dear colleagues,
It is my pleasure to announce IMPAC: an IMaging-PsychiAtry Challenge,
using data-science to predict autism from brain imaging.
https://paris-saclay-cds.github.io/autism_challenge/
This is a machine-learning challenge on brain-imaging data to achieve the
best prediction of
+1 on the post pointed out by Jeremiah.
On 5 May 2018 at 02:08, Johnson, Jeremiah wrote:
> Faraz, take a look at the discussion of this issue here:
> http://parrt.cs.usfca.edu/doc/rf-importance/index.html
>
> Best,
> Jeremiah
> =
Hi Aijaz,
On 05/05/18 07:31, aijaz qazi wrote:
> Dear developers of Scikit ,
Scikit is short for SciPy Toolkits (https://www.scipy.org/scikits.html);
there is a number of those. Scikit-learn started as one (and this is the
scikit-learn mailing list).
The package you are refering is based on