This package provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.
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),
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.
- Python 2.7+
- Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
- Fixed example.cfg path issue. Updated some documentation.
- Made path in make_example_iris_data.py consistent with the updated one in example.cfg
- Fixed bug where classification experiments would raise an error about class labels not being floats
- Updated documentation to include quick example for run_experiment.