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This is a Python wrapper for the fortran library used in the R package
`glmnet <http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`

__.
While the library includes linear, logistic, Cox, Poisson, and
multiple-response Gaussian, only linear and logistic are implemented in
this package.

The API follows the conventions of
`Scikit-Learn <http://scikit-learn.org/stable/>`

__, so it is expected to
work with tools from that ecosystem.

requirements

```
``python-glmnet`` requires Python version >= 3.6, ``scikit-learn``, ``numpy``,
and ``scipy``. Installation from source or via ``pip`` requires a Fortran compiler.
conda
~~~~~
.. code:: bash
conda install -c conda-forge glmnet
pip
~~~
.. code:: bash
pip install glmnet
source
~~~~~~
``glmnet`` depends on numpy, scikit-learn and scipy.
A working Fortran compiler is also required to build the package.
For Mac users, ``brew install gcc`` will take care of this requirement.
.. code:: bash
git clone git@github.com:civisanalytics/python-glmnet.git
cd python-glmnet
python setup.py install
Usage
-----
General
~~~~~~~
By default, ``LogitNet`` and ``ElasticNet`` fit a series of models using
the lasso penalty (α = 1) and up to 100 values for λ (determined by the
algorithm). In addition, after computing the path of λ values,
performance metrics for each value of λ are computed using 3-fold cross
validation. The value of λ corresponding to the best performing model is
saved as the ``lambda_max_`` attribute and the largest value of λ such
that the model performance is within ``cut_point * standard_error`` of
the best scoring model is saved as the ``lambda_best_`` attribute.
The ``predict`` and ``predict_proba`` methods accept an optional
parameter ``lamb`` which is used to select which model(s) will be used
to make predictions. If ``lamb`` is omitted, ``lambda_best_`` is used.
Both models will accept dense or sparse arrays.
Regularized Logistic Regression
```

.. code:: python

```
from glmnet import LogitNet
m = LogitNet()
m = m.fit(x, y)
```

Prediction is similar to Scikit-Learn:

.. code:: python

```
# predict labels
p = m.predict(x)
# or probability estimates
p = m.predict_proba(x)
```

Regularized Linear Regression

```
.. code:: python
from glmnet import ElasticNet
m = ElasticNet()
m = m.fit(x, y)
Predict:
.. code:: python
p = m.predict(x)
```

Great Documentation0

Easy to Use0

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