[scikit-learn] any interest in incorporating a new Transformer?
mcapizzi at email.arizona.edu
Sat Aug 19 18:36:21 EDT 2017
Thanks @joel -
I wasn’t aware of scikit-learn-contrib. Is this what you’re referring to?
If so, I don’t see any existing projects that this would fit into; could I
start a new one in a pull-request?
On Sat, Aug 19, 2017 at 2:47 AM, Joel Nothman <joel.nothman at gmail.com>
> this is the right place to ask, but I'd be more interested to see a
> scikit-learn-compatible implementation available, perhaps in
> scikit-learn-contrib more than to see it part of the main package...
> On 19 Aug 2017 2:13 am, "Michael Capizzi" <mcapizzi at email.arizona.edu>
>> Hi all -
>> Forgive me if this is the wrong place for posting this question, but I'd
>> like to inquire about the community's interest in incorporating a new
>> Transformer into the code base.
>> This paper ( https://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf )
>> is a "classic" in Natural Language Processing and is often times used as a
>> very competitive baseline. TL;DR it transforms a traditional count-based
>> feature space into the conditional probabilities of a `Naive Bayes`
>> classifier. These transformed features can then be used to train any
>> linear classifier. The paper focuses on `SVM`.
>> The attached notebook has an example of the custom `Transformer` I built
>> along with a custom `Classifier` to utilize this `Transformer` in a
>> `multiclass` case (as the feature space transformation differs depending on
>> the label).
>> If there is interest in the community for the inclusion of this
>> `Transformer` and `Classifier`, I'd happily go through the official process
>> of a `pull-request`, etc.
>> scikit-learn mailing list
>> scikit-learn at python.org
> scikit-learn mailing list
> scikit-learn at python.org
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