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# Classifier Comparison (Julia classifiers) # Comparing different clustering algorithms on toy datasets # Density Estimation for a mixture of Gaussians (using GaussianMixtures.jl) # Outlier detection with several methods # A demo of K-Means clustering on the handwritten digits data # Restricted Boltzmann Machine features for digit classification # Simple 1D Kernel Density Estimation # Sample pipeline for text feature extraction and evaluation # Two Class Adaboost # Underfitting vs. Overfitting

ScikitLearn.jl

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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.

Main features:

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.