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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

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

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 well-documented 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.

Requirements

Changelog

  • 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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