<a href="./examples/Classifier_Comparison_Julia.ipynb"><img src="./docs/example_images/Classifier_Comparison_Julia.png" alt="# Classifier Comparison (Julia classifiers)" width="170"> </a> <a href="./examples/Clustering_Comparison.ipynb"><img src="./docs/example_images/Clustering_Comparison.png" alt="# Comparing different clustering algorithms on toy datasets" width="170"> </a> <a href="./examples/Density_Estimation_Julia.ipynb"><img src="./docs/example_images/Density_Estimation_Julia.png" alt="# Density Estimation for a mixture of Gaussians (using GaussianMixtures.jl)" width="170"> </a> <a href="./examples/Outlier_Detection.ipynb"><img src="./docs/example_images/Outlier_Detection.png" alt="# Outlier detection with several methods" width="170"> </a> <a href="./examples/Plot_Kmeans_Digits.ipynb"><img src="./docs/example_images/Plot_Kmeans_Digits.png" alt="# A demo of K-Means clustering on the handwritten digits data" width="170"> </a> <a href="./examples/RBM.ipynb"><img src="./docs/example_images/RBM.png" alt="# Restricted Boltzmann Machine features for digit classification" width="170"> </a> <a href="./examples/Simple_1D_Kernel_Density.ipynb"><img src="./docs/example_images/Simple_1D_Kernel_Density.png" alt="# Simple 1D Kernel Density Estimation" width="170"> </a> <a href="./examples/Text_Feature_Extraction.ipynb"><img src="./docs/example_images/Text_image.png" alt="# Sample pipeline for text feature extraction and evaluation" width="170"> </a> <a href="./examples/Two_Class_Adaboost.ipynb"><img src="./docs/example_images/Two_Class_Adaboost.png" alt="# Two Class Adaboost" width="170"> </a> <a href="./examples/Underfitting_vs_Overfitting.ipynb"><img src="./docs/example_images/Underfitting_vs_Overfitting.png" alt="# Underfitting vs. Overfitting" width="170"> </a>

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

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

ROOT_ENV=`julia -e "using Conda; print(Conda.ROOTENV)`
export LD_LIBRARY_PATH=$ROOT_ENV"/lib":$LD_LIBRARY_PATH

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.