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Julia implementation of the scikit-learn API
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docs Update docs for DataFrames 0.11 Feb 8, 2018
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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


Documentation Status

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).

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.


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


See the manual and example gallery.


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.

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