sklearn2gem 0.1.1

# sklearn2gem

[](https://travis-ci.org/stewartpark/sklearn2gem) [](https://requires.io/github/stewartpark/sklearn2gem/requirements/?branch=master) [](https://badge.fury.io/py/sklearn2gem)

⚡ sklearn2gem ports your scikit-learn model into a fast ruby C binding!

# Getting started

Install sklearn2gem using pip:

` pip install sklearn2gem `

or via easy_install:

` easy_install sklearn2gem `

After that, dump your scikit-learn model with sklearn.externals.joblib, and run sklearn2gem model_name@version your_model.pkl foo/bar/model_name. You should be able to see a newly created folder named model_name under foo/bar/.

See [examples/iris.py](https://github.com/stewartpark/sklearn2gem/blob/master/examples/iris.py) to try it out.

To produce a pre-compiled binary gem, use [gem-compiler](https://github.com/luislavena/gem-compiler).

# What machine learning algorithms are supported?

Since sklearn2gem uses nok/sklearn-porter to convert a model into a C file, you can refer to [this page](https://github.com/nok/sklearn-porter#machine-learning-algorithms).