Class ENET

source code

Elastic-Net regression (ENET) `Classifier`.

Elastic-Net is the model selection algorithm from:

:ref:`Zou and Hastie (2005) <ZH05>` 'Regularization and Variable
Selection via the Elastic Net' Journal of the Royal Statistical
Society, Series B, 67, 301-320.

Similar to SMLR, it performs a feature selection while performing
classification, but instead of starting with all features, it
starts with none and adds them in, which is similar to boosting.

Unlike LARS it has both L1 and L2 regularization (instead of just
L1).  This means that while it tries to sparsify the features it
also tries to keep redundant features, which may be very very good
for fMRI classification.

In the true nature of the PyMVPA framework, this algorithm was
actually implemented in R by Zou and Hastie and wrapped via RPy.
To make use of ENET, you must have R and RPy installed as well as
both the lars and elasticnet contributed package. You can install
the R and RPy with the following command on Debian-based machines:

sudo aptitude install python-rpy python-rpy-doc r-base-dev

You can then install the lars and elasticnet package by running R
as root and calling:

install.packages()