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SciKit-Learn Laboratory provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.

Project description

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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

Changelog

  • v0.9.4
    • Documentation fixes
    • Added requirements.txt to manifest to fix broken PyPI release tarball.
  • v0.9.3
    • Fixed bug with merging feature sets that used to cause a crash.
    • If you’re running scikit-learn 0.14+, we use their StandardScaler, since the bug fix we include in FixedStandardScaler is in there.
    • Unit tests all pass again
    • Lots of little things related to using travis (which do not affect users)
  • v0.9.2
    • Fixed example.cfg path issue. Updated some documentation.
    • Made path in make_example_iris_data.py consistent with the updated one in example.cfg
  • v0.9.1
    • Fixed bug where classification experiments would raise an error about class labels not being floats
    • Updated documentation to include quick example for run_experiment.

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