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 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)
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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