<commit_before>.. currentmodule:: sklearn

.. _changes_0_13:

0.13
====

Changelog
---------

   - The table of contents has now been made expandible (on the
     index page) - by <NAME>.

   - :class:`feature_selection.SelectPercentile` now breaks ties
     deterministically instead of returning all equally ranked features.

   - Ridge regression and ridge classification fitting with ``sparse_cg`` solver
     no longer has quadratic memory complexity, by `<NAME>`_ and
     `<NAME>`_.

   - Ridge regression and ridge classification now support a new fast solver
     called ``lsqr``, by `<NAME>`_.

   - Speed up of :func:`metrics.precision_recall_curve` by <NAME>.

   - Added support for reading/writing svmlight files with pairwise
     preference attribute (qid in svmlight file format) in
     :func:`datasets.dump_svmlight_file` and
     :func:`datasets.load_svmlight_file` by `<NAME>`_.

   - New estimator :ref:`FeatureUnion <feature_union>` that concatenates results
     of several transformers by `<NAME>`_.

   - Faster and more robust :func:`metrics.confusion_matrix` and :ref:`clustering_evaluation`_
     by <NAME>.


API changes summary
-------------------

   - The module ``sklearn.linear_model.sparse`` is gone. Sparse matrix support
     was already integrated into the "regular" linear models.

   - ``sklearn.metrics.mean_square_error``, which incorrectly returned the
     cumulated error, was removed. Use ``mean_squared_error`` instead.

   - Passing ``class_weight`` parameters to ``fit`` methods is no longer
     supported. Pass them to estimator constuctors instead.

   - GMMs no longer have ``decode`` and ``rvs`` methods. Use the ``score``,
     ``predict`` or ``sample`` methods instead.

   - The ``solver`` fit option in Ridge regression and classification is now
     deprecated and will be removed in v0.14. Use the constructor option
     instead.

   - :class:`DictVectorizer` now returns sparse matrices in the CSR format,
     instead of COO.

.. _changes_0_12.1:

0.12.1
=======

The 0.12.1 release is a bug-fix release with no additional feature, but a
set of bug fixed

Changelog
----------

 - Improved numerical stability in spectral embedding by `<NAME>`_

 - Doctest under windows 64bit by `<NAME>`_

 - Documentation fixes for elastic net by `<NAME>`_ and
   `<NAME>`_

 - Proper behavior with fortran-ordered numpy arrays by `<NAME>`_

 - Make GridSearchCV work with non-CSR sparse matrix by `<NAME>`_

 - Fix parallel computing in MDS by `<NAME>`_

 - Fix unicode support in count vectorizer by `<NAME>`_

 - Fix MinCovDet breaking with X.shape = (3, 1) by `<NAME>`_
<commit_msg>DOC added changes / renames to whatsnew<commit_after>.. currentmodule:: sklearn

.. _changes_0_13:

0.13
====

Changelog
---------

   - The table of contents has now been made expandible (on the
     index page) - by <NAME>.

   - :class:`feature_selection.SelectPercentile` now breaks ties
     deterministically instead of returning all equally ranked features.

   - Ridge regression and ridge classification fitting with ``sparse_cg`` solver
     no longer has quadratic memory complexity, by `<NAME>`_ and
     `<NAME>`_.

   - Ridge regression and ridge classification now support a new fast solver
     called ``lsqr``, by `<NAME>`_.

   - Speed up of :func:`metrics.precision_recall_curve` by <NAME>.

   - Added support for reading/writing svmlight files with pairwise
     preference attribute (qid in svmlight file format) in
     :func:`datasets.dump_svmlight_file` and
     :func:`datasets.load_svmlight_file` by `<NAME>`_.

   - New estimator :ref:`FeatureUnion <feature_union>` that concatenates results
     of several transformers by `<NAME>`_.

   - Faster and more robust :func:`metrics.confusion_matrix` and
     :ref:`clustering_evaluation`_ by <NAME>.


API changes summary
-------------------

   - The module ``sklearn.linear_model.sparse`` is gone. Sparse matrix support
     was already integrated into the "regular" linear models.

   - ``sklearn.metrics.mean_square_error``, which incorrectly returned the
     cumulated error, was removed. Use ``mean_squared_error`` instead.

   - Passing ``class_weight`` parameters to ``fit`` methods is no longer
     supported. Pass them to estimator constuctors instead.

   - GMMs no longer have ``decode`` and ``rvs`` methods. Use the ``score``,
     ``predict`` or ``sample`` methods instead.

   - The ``solver`` fit option in Ridge regression and classification is now
     deprecated and will be removed in v0.14. Use the constructor option
     instead.

   - :class:`DictVectorizer` now returns sparse matrices in the CSR format,
     instead of COO.

   - Renamed ``k`` in :class:`cross_validation.KFold` and
     :class:`cross_validation.StratifiedKFold` to ``n_folds``, renamed
     ``n_bootstraps`` to ``n_iter`` in ``cross_validation.Bootstrap``.

   - Renamed all occurences of ``n_iterations`` to ``n_iter`` for consistency.
     This applies to :class:`cross_validation.ShuffleSplit`,
     :class:`cross_validation.StratifiedShuffleSplit`,
     :func:`utils.randomized_range_finder` and :func:`utils.randomized_svd`.

.. _changes_0_12.1:

0.12.1
=======

The 0.12.1 release is a bug-fix release with no additional feature, but a
set of bug fixed

Changelog
----------

 - Improved numerical stability in spectral embedding by `<NAME>`_

 - Doctest under windows 64bit by `<NAME>`_

 - Documentation fixes for elastic net by `<NAME>`_ and
   `<NAME>`_

 - Proper behavior with fortran-ordered numpy arrays by `<NAME>`_

 - Make GridSearchCV work with non-CSR sparse matrix by `<NAME>`_

 - Fix parallel computing in MDS by `<NAME>`_

 - Fix unicode support in count vectorizer by `<NAME>`_

 - Fix MinCovDet breaking with X.shape = (3, 1) by `<NAME>`_

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