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skll 0.25.0

SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.

Latest Version: 1.5.1

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 load_examples function and the Learner class. 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.



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


See GitHub releases.

File Type Py Version Uploaded on Size
skll-0.25.0-py2.py3-none-any.whl (md5) Python Wheel 3.3 2014-07-01 61KB
skll-0.25.0.tar.gz (md5) Source 2014-07-01 78KB