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

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.

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.

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

• Python 2.7+
• scikit-learn
• six
• PrettyTable
• BeautifulSoup 4
• Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
• configparser (only required for Python 2.7)
• futures (only required for Python 2.7)
• logutils (only required for Python 2.7)

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

See GitHub releases.