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There's some stuff already:<br>
<a class="moz-txt-link-freetext" href="https://github.com/SciRuby/">https://github.com/SciRuby/</a><br>
<br>
And in terms of strategy:<br>
No, you can go estimator by estimator and at some point implement
cross-validation and grid-search and pipelines and metrics pretty
independently.<br>
<br>
It looks like daru is written in ruby which I expect to be too slow.<br>
nmatrix is written in C++, so I guess you'd have to write many of
the algorithms in C++.<br>
<br>
At that point it might be easier to wrap an existing C++ library
like mlpack or shogun.<br>
<br>
<div class="moz-cite-prefix">On 2/5/19 6:12 AM, Joel Nothman wrote:<br>
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cite="mid:CAAkaFLVROW7tYuJh5gyiuZX5qC8391Cc_L4g+aAUp_B31+-dFA@mail.gmail.com">
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<div>If you count things in Scipy and NumPy (and Joblib and
Cython?) that Scikit-learn depends on and which may be
lacking or hard to find in SciRuby, it's much much more than
39 years. PyCall, and potentially some Scikit-learn-specific
wrappers around it, seems a much more sensible approach.</div>
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