``linlearn``: linear methods in Python ====================================== .. image:: https://travis-ci.org/linlearn/linlearn.svg?branch=master :target: https://travis-ci.org/linlearn/linlearn .. image:: https://readthedocs.org/projects/linlearn/badge/?version=latest :target: https://linlearn.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: https://img.shields.io/pypi/pyversions/linlearn :alt: PyPI - Python Version .. image:: https://img.shields.io/pypi/wheel/linlearn :alt: PyPI - Wheel .. image:: https://img.shields.io/github/stars/linlearn/linlearn :alt: GitHub stars :target: https://github.com/linlearn/linlearn/stargazers .. image:: https://img.shields.io/github/issues/linlearn/linlearn :alt: GitHub issues :target: https://github.com/linlearn/linlearn/issues .. image:: https://img.shields.io/github/license/linlearn/linlearn :alt: GitHub license :target: https://github.com/linlearn/linlearn/blob/master/LICENSE .. image:: https://coveralls.io/repos/github/linlearn/linlearn/badge.svg?branch=master :target: https://coveralls.io/github/linlearn/linlearn?branch=master ``linlearn`` stands for **linear learning**. It is a ``scikit-learn`` compatible python package for linear learning with Python. It provides : * Several estimators, including empirical risk minimization (which is the standard approach), median-of-means, trimmed means among others for robust regression and classification under the presence of outliers or heavy tails in your data. * Several loss functions easily accessible from a single class (``BinaryClassifier`` for binary classification and ``Regressor`` for regression) * Several penalization functions, including standard L1, ridge and elastic-net, but also total-variation, slope, weighted L1, among many others * All algorithms can use early stopping strategies during training * Supports dense and sparse data formats, and includes fast solvers for large sparse datasets (using state-of-the-art stochastic optimization algorithms) * It is accelerated thanks to numba, leading to a very concise, small, but fast library Installation ------------ The easiest way to install linlearn is using pip .. code-block:: bash pip install linlearn But you can also use the latest development from github directly with .. code-block:: bash pip install git+https://github.com/linlearn/linlearn.git References ---------- Usage ----- ``linlearn`` follows the scikit-learn API: you call fit instead of use ``predict_proba`` or ``predict`` whenever you need predictions. .. code-block:: python from linlearn import BinaryClassifier clf = BinaryClassifier() clf.fit(X_train, y_train) y_pred = clf.predict_proba(X_test)[:, 1] Where to go from here? ---------------------- .. toctree:: :maxdepth: 2 api classification