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 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.
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)
- joblib
- PyYAML
- configparser (only required for Python 2.7)
- logutils (only required for Python 2.7)
- mock (only required for Python 2.7)
- 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)
SKLL is featured in Data Science at the Command Line by Jeroen Janssens.
See GitHub releases.