Metadata-Version: 2.0
Name: sklearnsk
Version: 0.1.0
Summary: A wrapper for sklearn, that makes it easier to write, tune and evaluate classification and regression systems
Home-page: https://gitlab.com/sidekicklabs/sklearn-sidekick
Author: David Milne
Author-email: d.n.milne@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Keywords: machinelearning sklearn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: sklearn
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: check-manifest; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage; extra == 'test'

scikit-learn sidekick
=======================

A sidekick for scikit-learn that makes it easier to write, tune and evaluate classification and regression systems


Installation
------------

Install from the python package index::

    pip install sklearnsk

Or clone this repository and install::

    pip install .


Usage
-----

Check out the following notebooks in the example directory for examples of usage:

 * `iris.ipynb`: A toy classification problem
 * `boston.ipynb`: A toy regression problem
 * `20newsgroups.ipynb`: A more complex classification problem, involving n-grams, one-hot encoding, feature selection, etc.

Each of these examples will take you through the process of defining your system,
tuning it (with some nice visualisation), evaluating it, and performing additional analysis like feature ablation.


Licence
-------

This code is released under the MIT licence

