SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
Project description
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 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.
A Note on Pronunciation
SciKit-Learn Laboratory (SKLL) is pronounced “skull”: that’s where the learning happens.
Requirements
Python 2.7+
Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
configparser (only required for Python 2.7)
logutils (only required for Python 2.7)
mock (only required for Python 2.7)
Talks
Books
SKLL is featured in Data Science at the Command Line by Jeroen Janssens.
Changelog
See GitHub releases.
Release history Release notifications | RSS feed
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