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Ian Johnson
scikit-learn
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100faa39
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100faa39
authored
14 years ago
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Fabian Pedregosa
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Started Changelog 0.6.
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.. currentmodule:: scikits.learn
.. _changes_0_6:
0.6
===
scikits.learn 0.6 was released on december 2010. It is marked by the
inclusion of several new modules and a general renaming of old
ones. It is also marked by the inclusion of new example, including
applications to real-world datasets.
.. |banner1| image:: auto_examples/applications/images/plot_face_recognition.png
:height: 150
:target: auto_examples/applications/plot_face_recognition.html
.. |banner2| image:: auto_examples/applications/images/plot_species_distribution_modeling.png
:height: 150
:target: auto_examples/linear_model/plot_species_distribution.html
.. |banner3| image:: auto_examples/gaussian_process/images/plot_gp_regression.png
:height: 150
:target: auto_examples/gaussian_process/plot_gp_regression.html
.. |banner4| image:: auto_examples/linear_model/images/plot_sgd_iris.png
:height: 150
:target: auto_examples/linear_model/plot_lasso_lars.html
.. |center-div| raw:: html
<div style="text-align: center; margin: 0px 0 -5px 0;">
.. |end-div| raw:: html
</div>
|center-div| |banner1| |banner2| |banner3| |banner4| |end-div|
Changelog
---------
- New `stochastic gradient
<http://scikit-learn.sourceforge.net/modules/sgd.html>`_ descent
module by Peter Prettenhofer. The module comes with complete
documentation and some examples can
- Improved svm module: memory consumption has been reduced by 50%,
heuristic to automatically set class weights, possibility to
assign weights to samples (see
:ref:`example_svm_plot_weighted_samples.py` for an example).
- New :ref:`gaussian_process` module by Vincent Dubourg. This module
also has great documentation and some very neat examples. See
:ref:`example_gaussian_process_plot_gp_regression.py` or
:ref:`example_gaussian_process_plot_gp_probabilistic_classification_after_regression.py`
for a taste of what can be done.
- It is now possible to use liblinear’s Multi-class SVC (option
multi_class in :class:`linear_model.LinearSVC`)
- New features and performance improvements of text feature
extraction.
- Improved sparse matrix support, both in main classes
(:class:`grid_search.GridSearch`) as in modules
scikits.learn.svm.sparse and scikits.learn.linear_model.sparse.
- Lots of cool new examples and a new section that uses real-world
datasets was created. These include:
:ref:`example_applications_plot_face_recognition.py`,
:ref:`example_applications_plot_species_distribution_modeling.py`,
:ref:`example_applications_svm_gui.py`,
:ref:`example_applications_wikipedia_principal_eigenvector.py` and
others.
- Faster LARS algorithm. It is now 2x faster than the R version on
worst case and up to 10x times faster on some cases.
- Faster coordinate descent algorithm. In particular, the full path
version of lasso (:func:`linear_model.lasso_path`) is more than
200x times faster than before.
- It is now possible to get probability estimates from a
:class:`linear_model.LogisticRegression` model.
- module renaming: the glm module has been renamed to linear_model,
the gmm module has been included into the more general mixture
model and the sgd module has been included in linear_model.
- Lots of bug fixes and documentation improvements.
People
------
People that made this release possible preceeded by number of commits:
* 207 Olivier Grisel
* 138 `Fabian Pedregosa <http://fseoane.net/blog/>`_
* 97 `Peter Prettenhofer <http://sites.google.com/site/peterprettenhofer/>`_
* 68 `Alexandre Gramfort
<http://www-sop.inria.fr/members/Alexandre.Gramfort/index.fr.html>`_
* 59 `Mathieu Blondel <http://www.mblondel.org/journal/>`_
* 55 `Gael Varoquaux <http://gael-varoquaux.info/blog/>`_
* 33 Vincent Dubourg
* 21 `Ron Weiss <http://www.ee.columbia.edu/~ronw/>`_
* 9 Bertrand Thirion
* 3 `Alexandre Passos <http://atpassos.posterous.com>`_
* 3 Anne-Laure Fouque
* 2 Ronan Amicel
.. _changes_0_5:
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