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
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
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
A Note on Pronunciation
SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning happens.
- Python 2.7+
- 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)
See GitHub releases.