skll 0.23.1

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

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.

Requirements

• 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)

Talks

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

Changelog

See GitHub releases.