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 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+
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
Changelog
See GitHub releases.
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