/skll

SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

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SciKit-Learn Laboratory

Build status PyPI downloads Latest version on PyPI

License

DOI for citing SKLL 1.0.0

This Python package provides utilities to make it easier to run machine learning experiments with scikit-learn.

Command-line Interface

run_experiment is a command-line utility for running a series of learners on datasets specified in a configuration file. For more information about using run_experiment (including a quick example), go here.

Python API

If you just want to avoid writing a lot of boilerplate learning code, you can use our simple Python API. The main way you'll want to use the API is through the Learner and Reader classes. For more details on how to simply train, test, cross-validate, and run grid search on a variety of scikit-learn models see the documentation.

A Note on Pronunciation

SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning happens.

Requirements

Talks

  • Simpler Machine Learning with SKLL 1.0, Dan Blanchard, PyData NYC 2014 (slides)
  • Simpler Machine Learning with SKLL, Dan Blanchard, PyData NYC 2013 (video | slides)

Books

SKLL is featured in Data Science at the Command Line by Jeroen Janssens.

Changelog

See GitHub releases.