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.
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 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.
See GitHub releases.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|skll-0.27.0-py2.py3-none-any.whl (61.4 kB) Copy SHA256 hash SHA256||Wheel||3.4||Aug 13, 2014|
|skll-0.27.0.tar.gz (84.3 kB) Copy SHA256 hash SHA256||Source||None||Aug 13, 2014|