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See<br>
<a class="moz-txt-link-freetext" href="http://scikit-learn.org/dev/faq.html#what-are-the-inclusion-criteria-for-new-algorithms">http://scikit-learn.org/dev/faq.html#what-are-the-inclusion-criteria-for-new-algorithms</a><br>
and<br>
<a class="moz-txt-link-freetext" href="http://scikit-learn.org/dev/faq.html#why-is-there-no-support-for-deep-or-reinforcement-learning-will-there-be-support-for-deep-or-reinforcement-learning-in-scikit-learn">http://scikit-learn.org/dev/faq.html#why-is-there-no-support-for-deep-or-reinforcement-learning-will-there-be-support-for-deep-or-reinforcement-learning-in-scikit-learn</a><br>
<br>
Bandit algorithms require a fundamentally different kind of
interface than what's in scikit-learn right now, as they are
sequential decision making algorithms.<br>
<br>
<div class="moz-cite-prefix">On 09/04/2018 01:23 PM, Touqir Sajed
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CALfD4okUeyW0RuzdMZ_uQn_hMysc5c-9Lobiw1e_an6uA+j_rQ@mail.gmail.com">
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<div dir="ltr">
<div dir="ltr">Hi,
<div><br>
</div>
<div>This email is intended to initiate a discussion on
whether it is worth adding Multi-Armed Bandit (MAB)
algorithms in Scikit-learn. For those of you who have not
heard of MAB algorithms, they are the simplest form of
decision-making algorithms applicable whenever data with
labels are not given beforehand and the objective is to try
out different decisions, whenever a sample is seen, and
learn which decision is the best in the long run. They are
the simplest form of Reinforcement Learning algorithms.
While they are not applicable for every decision-making
tasks, they naturally fit into a number of problem settings
where they are more sample efficient and simpler than the
more advanced RL algorithms. For a number of applications : <a
href="https://ml-trckr.com/link/https%3A%2F%2Fwww.quora.com%2FIn-what-kind-of-real-life-situations-can-we-use-a-multi-arm-bandit-algorithm.%C2%A0If/0oMKOPmYKsXmGqYdOxqi"
moz-do-not-send="true">https://www.quora.com/In-what-kind-of-real-life-situations-can-we-use-a-multi-arm-bandit-algorithm. If</a>
you want to know more about their usage, how they work or
their advantages, feel free to let me know! </div>
<div><br>
</div>
<div>I do feel that MAB algorithms should be a part of
Scikit-learn since a lot of the interesting problems that we
face regarding learning is about decision making. There are
quite a few github repos with MAB implementations but their
coverage is extremely limited and I do not know of any
dedicated library on MABs. Companies like Yahoo, Microsoft,
Google use MABs for Ad recommendation and search engine
optimization but their code is not made public.</div>
<div><br>
</div>
<div>Cheers,</div>
<div>Touqir<br clear="all">
<div><br>
</div>
-- <br>
<div dir="ltr" class="gmail_signature">Computing Science
Master's student at University of Alberta, Canada,
specializing in Machine Learning. Website : <a
href="https://ml-trckr.com/link/https%3A%2F%2Fca.linkedin.com%2Fin%2Ftouqir-sajed-6a95b1126/0oMKOPmYKsXmGqYdOxqi"
target="_blank" moz-do-not-send="true">https://ca.linkedin.com/in/touqir-sajed-6a95b1126</a></div>
</div>
</div>
<img
src="https://ml-trckr.com/pixel/0oMKOPmYKsXmGqYdOxqi?rid=0oMKOPmYKsXmGqYdOxqi"
moz-do-not-send="true" border="0" height="1" width="1"></div>
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