ScikitLearn.jl
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.
Main features:
- Around 150 Julia and Python models accessed through a uniform interface
- Pipelines and FeatureUnions
- Cross-validation
- Hyperparameter tuning
- DataFrames support
Check out the Quick-Start Guide for a tour.
Installation
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
Documentation
See the manual and example gallery.
Goal
ScikitLearn.jl aims for feature parity with scikit-learn. If you encounter any problem that is solved by that library but not this one, file an issue.