Changelog for python3-scikit-learn-0.18.1-2.17.x86_64.rpm :
Sun Jun 11 14:00:00 2017 toddrme2178AATTgmail.com
- Implement single-spec version
- Update source URL
- Update to version 0.18.1

* Large number of changes. See:
https://github.com/scikit-learn/scikit-learn/blob/0.18.1/doc/whats_new.rst

Mon Jan 11 13:00:00 2016 toddrme2178AATTgmail.com
- Switch to proper package name: python-scikit-learn

Fri Nov 20 13:00:00 2015 Angelos Tzotsos
- Update to version 0.17

Thu Oct 24 14:00:00 2013 toddrme2178AATTgmail.com
- Update to version 14.1

* Minor bugfixes
- Update to version 14.0

* Changelog
- Missing values with sparse and dense matrices can be imputed with the
transformer :class:`preprocessing.Imputer` by `Nicolas Trésegnie`_.
- The core implementation of decisions trees has been rewritten from
scratch, allowing for faster tree induction and lower memory
consumption in all tree-based estimators. By `Gilles Louppe`_.
- Added :class:`ensemble.AdaBoostClassifier` and
:class:`ensemble.AdaBoostRegressor`, by `Noel Dawe`_ and
`Gilles Louppe`_. See the :ref:`AdaBoost ` section of the user
guide for details and examples.
- Added :class:`grid_search.RandomizedSearchCV` and
:class:`grid_search.ParameterSampler` for randomized hyperparameter
optimization. By `Andreas Müller`_.
- Added :ref:`biclustering ` algorithms
(:class:`sklearn.cluster.bicluster.SpectralCoclustering` and
:class:`sklearn.cluster.bicluster.SpectralBiclustering`), data
generation methods (:func:`sklearn.datasets.make_biclusters` and
:func:`sklearn.datasets.make_checkerboard`), and scoring metrics
(:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_.
- Added :ref:`Restricted Boltzmann Machines`
(:class:`neural_network.BernoulliRBM`). By `Yann Dauphin`_.
- Python 3 support by `Justin Vincent`_, `Lars Buitinck`_,
`Subhodeep Moitra`_ and `Olivier Grisel`_. All tests now pass under
Python 3.3.
- Ability to pass one penalty (alpha value) per target in
:class:`linear_model.Ridge`, by AATTeickenberg and `Mathieu Blondel`_.
- Fixed :mod:`sklearn.linear_model.stochastic_gradient.py` L2 regularization
issue (minor practical significants).
By `Norbert Crombach`_ and `Mathieu Blondel`_ .
- Added an interactive version of `Andreas Müller`_\'s
`Machine Learning Cheat Sheet (for scikit-learn)
`_
to the documentation. See :ref:`Choosing the right estimator `.
By `Jaques Grobler`_.
- :class:`grid_search.GridSearchCV` and
:func:`cross_validation.cross_val_score` now support the use of advanced
scoring function such as area under the ROC curve and f-beta scores.
See :ref:`scoring_parameter` for details. By `Andreas Müller`_
and `Lars Buitinck`_.
Passing a function from :mod:`sklearn.metrics` as ``score_func`` is
deprecated.
- Multi-label classification output is now supported by
:func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`,
:func:`metrics.f1_score`, :func:`metrics.fbeta_score`,
:func:`metrics.classification_report`,
:func:`metrics.precision_score` and :func:`metrics.recall_score`
by `Arnaud Joly`_.
- Two new metrics :func:`metrics.hamming_loss` and
:func:`metrics.jaccard_similarity_score`
are added with multi-label support by `Arnaud Joly`_.
- Speed and memory usage improvements in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`,
by Jochen Wersdörfer and Roman Sinayev.
- The ``min_df`` parameter in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`, which used to be 2,
has been reset to 1 to avoid unpleasant surprises (empty vocabularies)
for novice users who try it out on tiny document collections.
A value of at least 2 is still recommended for practical use.
- :class:`svm.LinearSVC`, :class:`linear_model.SGDClassifier` and
:class:`linear_model.SGDRegressor` now have a ``sparsify`` method that
converts their ``coef_`` into a sparse matrix, meaning stored models
trained using these estimators can be made much more compact.
- :class:`linear_model.SGDClassifier` now produces multiclass probability
estimates when trained under log loss or modified Huber loss.
- Hyperlinks to documentation in example code on the website by
`Martin Luessi`_.
- Fixed bug in :class:`preprocessing.MinMaxScaler` causing incorrect scaling
of the features for non-default ``feature_range`` settings. By `Andreas
Müller`_.
- ``max_features`` in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
now supports percentage values. By `Gilles Louppe`_.
- Performance improvements in :class:`isotonic.IsotonicRegression` by
`Nelle Varoquaux`_.
- :func:`metrics.accuracy_score` has an option normalize to return
the fraction or the number of correctly classified sample
by `Arnaud Joly`_.
- Added :func:`metrics.log_loss` that computes log loss, aka cross-entropy
loss. By Jochen Wersdörfer and `Lars Buitinck`_.
- A bug that caused :class:`ensemble.AdaBoostClassifier`\'s to output
incorrect probabilities has been fixed.
- Feature selectors now share a mixin providing consistent `transform`,
`inverse_transform` and `get_support` methods. By `Joel Nothman`_.
- A fitted :class:`grid_search.GridSearchCV` or
:class:`grid_search.RandomizedSearchCV` can now generally be pickled.
By `Joel Nothman`_.
- Refactored and vectorized implementation of :func:`metrics.roc_curve`
and :func:`metrics.precision_recall_curve`. By `Joel Nothman`_.
- The new estimator :class:`sklearn.decomposition.TruncatedSVD`
performs dimensionality reduction using SVD on sparse matrices,
and can be used for latent semantic analysis (LSA).
By `Lars Buitinck`_.
- Added self-contained example of out-of-core learning on text data
:ref:`example_applications_plot_out_of_core_classification.py`.
By `Eustache Diemert`_.
- The default number of components for
:class:`sklearn.decomposition.RandomizedPCA` is now correctly documented
to be ``n_features``. This was the default behavior, so programs using it
will continue to work as they did.
- :class:`sklearn.cluster.KMeans` now fits several orders of magnitude
faster on sparse data (the speedup depends on the sparsity). By
`Lars Buitinck`_.
- Reduce memory footprint of FastICA by `Denis Engemann`_ and
`Alexandre Gramfort`_.
- Verbose output in :mod:`sklearn.ensemble.gradient_boosting` now uses
a column format and prints progress in decreasing frequency.
It also shows the remaining time. By `Peter Prettenhofer`_.
- :mod:`sklearn.ensemble.gradient_boosting` provides out-of-bag improvement
:attr:`~sklearn.ensemble.GradientBoostingRegressor.oob_improvement_`
rather than the OOB score for model selection. An example that shows
how to use OOB estimates to select the number of trees was added.
By `Peter Prettenhofer`_.
- Most metrics now support string labels for multiclass classification
by `Arnaud Joly`_ and `Lars Buitinck`_.
- New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_
and `Vlad Niculae`_.
- Fixed a bug in :class:`sklearn.covariance.GraphLassoCV`: the
\'alphas\' parameter now works as expected when given a list of
values. By Philippe Gervais.
- Fixed an important bug in :class:`sklearn.covariance.GraphLassoCV`
that prevented all folds provided by a CV object to be used (only
the first 3 were used). When providing a CV object, execution
time may thus increase significantly compared to the previous
version (bug results are correct now). By Philippe Gervais.
- :class:`cross_validation.cross_val_score` and the :mod:`grid_search`
module is now tested with multi-output data by `Arnaud Joly`_.
- :func:`datasets.make_multilabel_classification` can now return
the output in label indicator multilabel format by `Arnaud Joly`_.
- K-nearest neighbors, :class:`neighbors.KNeighborsRegressor`
and :class:`neighbors.RadiusNeighborsRegressor`,
and radius neighbors, :class:`neighbors.RadiusNeighborsRegressor` and
:class:`neighbors.RadiusNeighborsClassifier` support multioutput data
by `Arnaud Joly`_.
- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`NuSVC`,
:class:`OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be
controlled. This is useful to ensure consistency in the probability
estimates for the classifiers trained with ``probability=True``. By
`Vlad Niculae`_.
- Out-of-core learning support for discrete naive Bayes classifiers
:class:`sklearn.naive_bayes.MultinomialNB` and
:class:`sklearn.naive_bayes.BernoulliNB` by adding the ``partial_fit``
method by `Olivier Grisel`_.
- New website design and navigation by `Gilles Louppe`_, `Nelle Varoquaux`_,
Vincent Michel and `Andreas Müller`_.
- Improved documentation on :ref:`multi-class, multi-label and multi-output
classification ` by `Yannick Schwartz`_ and `Arnaud Joly`_.
- Better input and error handling in the :mod:`metrics` module by
`Arnaud Joly`_ and `Joel Nothman`_.
- Speed optimization of the :mod:`hmm` module by `Mikhail Korobov`_
- Significant speed improvements for :class:`sklearn.cluster.DBSCAN`_
by `cleverless `_

* API changes:
- The :func:`auc_score` was renamed :func:`roc_auc_score`.
- Testing scikit-learn with `sklearn.test()` is deprecated. Use
`nosetest sklearn` from the command line.
- Feature importances in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
are now computed on the fly when accessing the ``feature_importances_``
attribute. Setting ``compute_importances=True`` is no longer required.
By `Gilles Louppe`_.
- :class:`linear_model.lasso_path` and
:class:`linear_model.enet_path` can return its results in the same
format as that of :class:`linear_model.lars_path`. This is done by
setting the `return_models` parameter to `False`. By
`Jaques Grobler`_ and `Alexandre Gramfort`_
- :class:`grid_search.IterGrid` was renamed to
:class:`grid_search.ParameterGrid`.
- Fixed bug in :class:`KFold` causing imperfect class balance in some
cases. By `Alexandre Gramfort`_ and Tadej Janež.
- :class:`sklearn.neighbors.BallTree` has been refactored, and a
:class:`sklearn.neighbors.KDTree` has been
added which shares the same interface. The Ball Tree now works with
a wide variety of distance metrics. Both classes have many new
methods, including single-tree and dual-tree queries, breadth-first
and depth-first searching, and more advanced queries such as
kernel density estimation and 2-point correlation functions.
By `Jake Vanderplas`_
- Support for scipy.spatial.cKDTree within neighbors queries has been
removed, and the functionality replaced with the new :class:`KDTree`
class.
- :class:`sklearn.neighbors.KernelDensity` has been added, which performs
efficient kernel density estimation with a variety of kernels.
- :class:`sklearn.decomposition.KernelPCA` now always returns output with
``n_components`` components, unless the new parameter ``remove_zero_eig``
is set to ``True``. This new behavior is consistent with the way
kernel PCA was always documented; previously, the removal of components
with zero eigenvalues was tacitly performed on all data.
- ``gcv_mode=\"auto\"`` no longer tries to perform SVD on a densified
sparse matrix in :class:`sklearn.linear_model.RidgeCV`.
- Sparse matrix support in :class:`sklearn.decomposition.RandomizedPCA`
is now deprecated in favor of the new ``TruncatedSVD``.
- :class:`cross_validation.KFold` and
:class:`cross_validation.StratifiedKFold` now enforce `n_folds >= 2`
otherwise a ``ValueError`` is raised. By `Olivier Grisel`_.
- :func:`datasets.load_files`\'s ``charset`` and ``charset_errors``
parameters were renamed ``encoding`` and ``decode_errors``.
- Attribute ``oob_score_`` in :class:`sklearn.ensemble.GradientBoostingRegressor`
and :class:`sklearn.ensemble.GradientBoostingClassifier`
is deprecated and has been replaced by ``oob_improvement_`` .
- Attributes in OrthogonalMatchingPursuit have been deprecated
(copy_X, Gram, ...) and precompute_gram renamed precompute
for consistency. See #2224.
- :class:`sklearn.preprocessing.StandardScaler` now converts integer input
to float, and raises a warning. Previously it rounded for dense integer
input.
- Better input validation, warning on unexpected shapes for y.
- Fix building on 13.1+
- Update BuildRequires
- Cleanup spec file formatting

Thu Oct 24 14:00:00 2013 speilickeAATTsuse.com
- Require python-setuptools instead of distribute (upstreams merged)

Fri May 3 14:00:00 2013 toddrme2178AATTgmail.com
- Update to version 0.13.1

Sat Oct 13 14:00:00 2012 Angelos Tzotsos
- Update to version 0.12.1

Sun Jun 3 14:00:00 2012 toddrme2178AATTgmail.com
- Clean up spec file
- Update to version 0.11

Wed Mar 7 13:00:00 2012 scorotAATTfree.fr
- remove unneeded libatals3-devel dependency

Mon Oct 10 14:00:00 2011 scorotAATTgtt.fr
- fix python-Sphinx requirement

Sat Oct 23 14:00:00 2010 scorotAATTgtt.fr
- first package
- version 0.5