There's some stuff already:
https://github.com/SciRuby/
And in terms of strategy:
No, you can go estimator by estimator and at some point implement
cross-validation and grid-search and pipelines and metrics pretty
independently.
It looks like daru is written in ruby which I expect to be too slow.
nmatrix is written in C++, so I guess you'd have to write many of the
algorithms in C++.
At that point it might be easier to wrap an existing C++ library like
mlpack or shogun.
On 2/5/19 6:12 AM, Joel Nothman wrote:
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.
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