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HOAG

Hyperparameter optimization with approximate gradient

.. image:: https://raw.githubusercontent.com/fabianp/hoag/master/doc/comparison_ho_real_sim.png :scale: 50 %

Depends

• scikit-learn 0.16

Usage

This package exports a LogisticRegressionCV class which automatically estimates the L2 regularization of logistic regression. As other scikit-learn objects, it has a .fit and .predict method. However, unlike scikit-learn objects, the .fit method takes 4 arguments consisting of the train set and the test set. For example:

>>> from hoag import LogisticRegressionCV
>>> clf = LogisticRegressionCV()
>>> clf.fit(X_train, y_train, X_test, y_test)

where X_train, y_train, X_test, y_test are numpy arrays representing the train and test set, respectively.

For full usage example check out this ipython notebook <https://github.com/fabianp/hoag/blob/master/doc/example_usage.ipynb>_.

.. image:: https://raw.githubusercontent.com/fabianp/hoag/master/doc/hoag_screenshot.png :target: https://github.com/fabianp/hoag/blob/master/doc/example_usage.ipynb

Usage tips

Standardize features of the input data such that each feature has unit variance. This makes the Hessian better conditioned. This can be done using e.g. scikit-learn's StandardScaler.

Citing

If you use this, please cite it as

.. code-block::

@inproceedings{PedregosaHyperparameter16, author = {Fabian Pedregosa}, title = {Hyperparameter optimization with approximate gradient}, booktitle = {Proceedings of the 33nd International Conference on Machine Learning, {ICML}}, year = {2016}, url = {http://jmlr.org/proceedings/papers/v48/pedregosa16.html}, }