[scikit-learn] scikit-learn Digest, Vol 12, Issue 18

[Gautam Borad]

On 11 Mar 2017 22:32, <scikit-learn-request at python.org> wrote:

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> Today's Topics:
>
>    1. Label encoding for classifiers and soft targets
>       (Javier L?pez Pe?a)
>    2. issue suggestion - decision trees - GSoC (Konstantinos Katrioplas)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 11 Mar 2017 13:04:57 +0000
> From: Javier L?pez Pe?a <jlopez at ende.cc>
> To: scikit-learn at python.org
> Subject: [scikit-learn] Label encoding for classifiers and soft
>         targets
> Message-ID: <542B0BDD-F329-4F26-9001-9F535426306C at ende.cc>
> Content-Type: text/plain; charset=utf-8
>
> Hi there!
>
> I have been recently experimenting with model regularization through the
> use of soft targets,
> and I?d like to be able to play with that from sklearn.
>
> The main idea is as follows: imagine I want to fit a (probabilisitic)
> classifier with three possible
> targets, 0, 1, 2
>
> If I pass my training set (X, y) to a sklearn classifier, the target
> vector y gets encoded so that
> each target becomes an array, [1, 0, 0], [0, 1, 0], or [0, 0, 1]
>
> What I would like to do is to be able to pass the targets directly in the
> encoded form, and avoid
> any further encoding. This allows for instance to pass targets as [0.9,
> 0.5, 0.5] if I want to prevent
> my classifier from becoming too opinionated on its predicted probabilities.
>
> Ideally I would like to do something like this:
> ```
> clf = SomeClassifier(*parameters, encode_targets=False)
> ```
>
> and then call
> ```
> elf.fit(X, encoded_y)
> ```
>
> Would it be simple to modify sklearn code to do this, or would it require
> a lot of tinkering
> such as modifying every single classifier under the sun?
>
> Cheers,
> J
>
> ------------------------------
>
> Message: 2
> Date: Sat, 11 Mar 2017 15:29:30 +0200
> From: Konstantinos Katrioplas <konst.katrioplas at gmail.com>
> To: scikit-learn at python.org
> Subject: [scikit-learn] issue suggestion - decision trees - GSoC
> Message-ID: <33a3a5bf-37dd-1cad-c4ae-ef4b62294a8c at gmail.com>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Hello all,
>
> While I am waiting for the PR that I have submitted to be evaluated
> (https://github.com/scikit-learn/scikit-learn/pull/8563), would you
> suggest another (easy) issue for me to work on? Ideally something for
> which I will write some substantial code, so as to present it in my
> application for GSoC?
>
> Is anyone interested to mentor me in the parallelization of decision
> trees? I admit I am not yet really familiar with the current tree code
> (although I have been using the method for regression on a research
> project) but I am very much intrigued by the idea and willing to learn
> all about it until the summer.
>
> Regards,
> Konstantinos
>
>
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> ------------------------------
>
> End of scikit-learn Digest, Vol 12, Issue 18
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