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ScikitLearn.jl implements the popular
scikit-learn interface and algorithms in
Julia. It supports both models from the Julia ecosystem and those of the
scikit-learn library
(via PyCall.jl).
Would you rather use a machine-learning framework specially-designed for Julia? Check out MLJ.jl, from the Alan Turing institute.
Disclaimer: ScikitLearn.jl borrows code and documentation from
scikit-learn, but it is not an official part
of that project. It is licensed under BSD-3.
To install ScikitLearn.jl, type ]add ScikitLearn at the REPL.
To import Python models (optional), ScikitLearn.jl requires the scikit-learn Python library, which will be installed automatically when needed. Most of the examples use PyPlot.jl
Known issue
On Linux builds, importing python models via @sk_import is known to fail for Julia v<0.8.4 when the PYTHON enviroment variable from PyCall.jl is set to "" or conda. This is becuase the version libstdcxx loaded by Julia v<0.8.4 isn't compatible with the version of scikit-learn installed via Conda.
The easiest and recommended way to resolve this is to upgrade to Julia v>=1.8.4. If you must stick with your current julia version you can also resolve this issue by pre-appending your system's LD_LIBRARY_PATH enviroment variable as shown below