516 lines
22 KiB
ReStructuredText
516 lines
22 KiB
ReStructuredText
.. include:: _contributors.rst
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.. currentmodule:: sklearn
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============
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Version 0.17
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============
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.. _changes_0_17_1:
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Version 0.17.1
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==============
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**February 18, 2016**
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Changelog
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---------
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Bug fixes
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.........
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- Upgrade vendored joblib to version 0.9.4 that fixes an important bug in
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``joblib.Parallel`` that can silently yield to wrong results when working
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on datasets larger than 1MB:
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https://github.com/joblib/joblib/blob/0.9.4/CHANGES.rst
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- Fixed reading of Bunch pickles generated with scikit-learn
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version <= 0.16. This can affect users who have already
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downloaded a dataset with scikit-learn 0.16 and are loading it
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with scikit-learn 0.17. See :issue:`6196` for
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how this affected :func:`datasets.fetch_20newsgroups`. By `Loic
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Esteve`_.
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- Fixed a bug that prevented using ROC AUC score to perform grid search on
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several CPU / cores on large arrays. See :issue:`6147`
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By `Olivier Grisel`_.
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- Fixed a bug that prevented to properly set the ``presort`` parameter
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in :class:`ensemble.GradientBoostingRegressor`. See :issue:`5857`
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By Andrew McCulloh.
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- Fixed a joblib error when evaluating the perplexity of a
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:class:`decomposition.LatentDirichletAllocation` model. See :issue:`6258`
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By Chyi-Kwei Yau.
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.. _changes_0_17:
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Version 0.17
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============
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**November 5, 2015**
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Changelog
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---------
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New features
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............
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- All the Scaler classes but :class:`preprocessing.RobustScaler` can be fitted online by
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calling `partial_fit`. By :user:`Giorgio Patrini <giorgiop>`.
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- The new class :class:`ensemble.VotingClassifier` implements a
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"majority rule" / "soft voting" ensemble classifier to combine
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estimators for classification. By `Sebastian Raschka`_.
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- The new class :class:`preprocessing.RobustScaler` provides an
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alternative to :class:`preprocessing.StandardScaler` for feature-wise
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centering and range normalization that is robust to outliers.
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By :user:`Thomas Unterthiner <untom>`.
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- The new class :class:`preprocessing.MaxAbsScaler` provides an
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alternative to :class:`preprocessing.MinMaxScaler` for feature-wise
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range normalization when the data is already centered or sparse.
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By :user:`Thomas Unterthiner <untom>`.
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- The new class :class:`preprocessing.FunctionTransformer` turns a Python
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function into a ``Pipeline``-compatible transformer object.
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By Joe Jevnik.
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- The new classes `cross_validation.LabelKFold` and
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`cross_validation.LabelShuffleSplit` generate train-test folds,
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respectively similar to `cross_validation.KFold` and
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`cross_validation.ShuffleSplit`, except that the folds are
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conditioned on a label array. By `Brian McFee`_, :user:`Jean
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Kossaifi <JeanKossaifi>` and `Gilles Louppe`_.
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- :class:`decomposition.LatentDirichletAllocation` implements the Latent
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Dirichlet Allocation topic model with online variational
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inference. By :user:`Chyi-Kwei Yau <chyikwei>`, with code based on an implementation
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by Matt Hoffman. (:issue:`3659`)
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- The new solver ``sag`` implements a Stochastic Average Gradient descent
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and is available in both :class:`linear_model.LogisticRegression` and
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:class:`linear_model.Ridge`. This solver is very efficient for large
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datasets. By :user:`Danny Sullivan <dsullivan7>` and `Tom Dupre la Tour`_.
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(:issue:`4738`)
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- The new solver ``cd`` implements a Coordinate Descent in
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:class:`decomposition.NMF`. Previous solver based on Projected Gradient is
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still available setting new parameter ``solver`` to ``pg``, but is
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deprecated and will be removed in 0.19, along with
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`decomposition.ProjectedGradientNMF` and parameters ``sparseness``,
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``eta``, ``beta`` and ``nls_max_iter``. New parameters ``alpha`` and
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``l1_ratio`` control L1 and L2 regularization, and ``shuffle`` adds a
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shuffling step in the ``cd`` solver.
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By `Tom Dupre la Tour`_ and `Mathieu Blondel`_.
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Enhancements
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............
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- :class:`manifold.TSNE` now supports approximate optimization via the
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Barnes-Hut method, leading to much faster fitting. By Christopher Erick Moody.
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(:issue:`4025`)
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- :class:`cluster.MeanShift` now supports parallel execution,
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as implemented in the ``mean_shift`` function. By :user:`Martino
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Sorbaro <martinosorb>`.
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- :class:`naive_bayes.GaussianNB` now supports fitting with ``sample_weight``.
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By `Jan Hendrik Metzen`_.
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- :class:`dummy.DummyClassifier` now supports a prior fitting strategy.
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By `Arnaud Joly`_.
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- Added a ``fit_predict`` method for `mixture.GMM` and subclasses.
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By :user:`Cory Lorenz <clorenz7>`.
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- Added the :func:`metrics.label_ranking_loss` metric.
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By `Arnaud Joly`_.
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- Added the :func:`metrics.cohen_kappa_score` metric.
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- Added a ``warm_start`` constructor parameter to the bagging ensemble
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models to increase the size of the ensemble. By :user:`Tim Head <betatim>`.
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- Added option to use multi-output regression metrics without averaging.
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By Konstantin Shmelkov and :user:`Michael Eickenberg<eickenberg>`.
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- Added ``stratify`` option to `cross_validation.train_test_split`
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for stratified splitting. By Miroslav Batchkarov.
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- The :func:`tree.export_graphviz` function now supports aesthetic
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improvements for :class:`tree.DecisionTreeClassifier` and
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:class:`tree.DecisionTreeRegressor`, including options for coloring nodes
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by their majority class or impurity, showing variable names, and using
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node proportions instead of raw sample counts. By `Trevor Stephens`_.
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- Improved speed of ``newton-cg`` solver in
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:class:`linear_model.LogisticRegression`, by avoiding loss computation.
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By `Mathieu Blondel`_ and `Tom Dupre la Tour`_.
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- The ``class_weight="auto"`` heuristic in classifiers supporting
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``class_weight`` was deprecated and replaced by the ``class_weight="balanced"``
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option, which has a simpler formula and interpretation.
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By `Hanna Wallach`_ and `Andreas Müller`_.
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- Add ``class_weight`` parameter to automatically weight samples by class
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frequency for :class:`linear_model.PassiveAggressiveClassifier`. By
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`Trevor Stephens`_.
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- Added backlinks from the API reference pages to the user guide. By
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`Andreas Müller`_.
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- The ``labels`` parameter to :func:`sklearn.metrics.f1_score`,
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:func:`sklearn.metrics.fbeta_score`,
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:func:`sklearn.metrics.recall_score` and
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:func:`sklearn.metrics.precision_score` has been extended.
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It is now possible to ignore one or more labels, such as where
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a multiclass problem has a majority class to ignore. By `Joel Nothman`_.
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- Add ``sample_weight`` support to :class:`linear_model.RidgeClassifier`.
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By `Trevor Stephens`_.
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- Provide an option for sparse output from
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:func:`sklearn.metrics.pairwise.cosine_similarity`. By
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:user:`Jaidev Deshpande <jaidevd>`.
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- Add :func:`preprocessing.minmax_scale` to provide a function interface for
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:class:`preprocessing.MinMaxScaler`. By :user:`Thomas Unterthiner <untom>`.
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- ``dump_svmlight_file`` now handles multi-label datasets.
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By Chih-Wei Chang.
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- RCV1 dataset loader (:func:`sklearn.datasets.fetch_rcv1`).
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By `Tom Dupre la Tour`_.
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- The "Wisconsin Breast Cancer" classical two-class classification dataset
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is now included in scikit-learn, available with
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:func:`datasets.load_breast_cancer`.
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- Upgraded to joblib 0.9.3 to benefit from the new automatic batching of
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short tasks. This makes it possible for scikit-learn to benefit from
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parallelism when many very short tasks are executed in parallel, for
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instance by the `grid_search.GridSearchCV` meta-estimator
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with ``n_jobs > 1`` used with a large grid of parameters on a small
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dataset. By `Vlad Niculae`_, `Olivier Grisel`_ and `Loic Esteve`_.
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- For more details about changes in joblib 0.9.3 see the release notes:
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https://github.com/joblib/joblib/blob/master/CHANGES.rst#release-093
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- Improved speed (3 times per iteration) of
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`decomposition.DictLearning` with coordinate descent method
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from :class:`linear_model.Lasso`. By :user:`Arthur Mensch <arthurmensch>`.
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- Parallel processing (threaded) for queries of nearest neighbors
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(using the ball-tree) by Nikolay Mayorov.
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- Allow :func:`datasets.make_multilabel_classification` to output
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a sparse ``y``. By Kashif Rasul.
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- :class:`cluster.DBSCAN` now accepts a sparse matrix of precomputed
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distances, allowing memory-efficient distance precomputation. By
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`Joel Nothman`_.
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- :class:`tree.DecisionTreeClassifier` now exposes an ``apply`` method
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for retrieving the leaf indices samples are predicted as. By
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:user:`Daniel Galvez <galv>` and `Gilles Louppe`_.
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- Speed up decision tree regressors, random forest regressors, extra trees
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regressors and gradient boosting estimators by computing a proxy
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of the impurity improvement during the tree growth. The proxy quantity is
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such that the split that maximizes this value also maximizes the impurity
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improvement. By `Arnaud Joly`_, :user:`Jacob Schreiber <jmschrei>`
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and `Gilles Louppe`_.
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- Speed up tree based methods by reducing the number of computations needed
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when computing the impurity measure taking into account linear
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relationship of the computed statistics. The effect is particularly
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visible with extra trees and on datasets with categorical or sparse
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features. By `Arnaud Joly`_.
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- :class:`ensemble.GradientBoostingRegressor` and
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:class:`ensemble.GradientBoostingClassifier` now expose an ``apply``
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method for retrieving the leaf indices each sample ends up in under
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each try. By :user:`Jacob Schreiber <jmschrei>`.
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- Add ``sample_weight`` support to :class:`linear_model.LinearRegression`.
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By Sonny Hu. (:issue:`#4881`)
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- Add ``n_iter_without_progress`` to :class:`manifold.TSNE` to control
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the stopping criterion. By Santi Villalba. (:issue:`5186`)
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- Added optional parameter ``random_state`` in :class:`linear_model.Ridge`
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, to set the seed of the pseudo random generator used in ``sag`` solver. By `Tom Dupre la Tour`_.
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- Added optional parameter ``warm_start`` in
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:class:`linear_model.LogisticRegression`. If set to True, the solvers
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``lbfgs``, ``newton-cg`` and ``sag`` will be initialized with the
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coefficients computed in the previous fit. By `Tom Dupre la Tour`_.
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- Added ``sample_weight`` support to :class:`linear_model.LogisticRegression` for
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the ``lbfgs``, ``newton-cg``, and ``sag`` solvers. By `Valentin Stolbunov`_.
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Support added to the ``liblinear`` solver. By `Manoj Kumar`_.
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- Added optional parameter ``presort`` to :class:`ensemble.GradientBoostingRegressor`
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and :class:`ensemble.GradientBoostingClassifier`, keeping default behavior
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the same. This allows gradient boosters to turn off presorting when building
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deep trees or using sparse data. By :user:`Jacob Schreiber <jmschrei>`.
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- Altered :func:`metrics.roc_curve` to drop unnecessary thresholds by
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default. By :user:`Graham Clenaghan <gclenaghan>`.
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- Added :class:`feature_selection.SelectFromModel` meta-transformer which can
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be used along with estimators that have `coef_` or `feature_importances_`
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attribute to select important features of the input data. By
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:user:`Maheshakya Wijewardena <maheshakya>`, `Joel Nothman`_ and `Manoj Kumar`_.
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- Added :func:`metrics.pairwise.laplacian_kernel`. By `Clyde Fare <https://github.com/Clyde-fare>`_.
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- `covariance.GraphLasso` allows separate control of the convergence criterion
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for the Elastic-Net subproblem via the ``enet_tol`` parameter.
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- Improved verbosity in :class:`decomposition.DictionaryLearning`.
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- :class:`ensemble.RandomForestClassifier` and
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:class:`ensemble.RandomForestRegressor` no longer explicitly store the
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samples used in bagging, resulting in a much reduced memory footprint for
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storing random forest models.
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- Added ``positive`` option to :class:`linear_model.Lars` and
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:func:`linear_model.lars_path` to force coefficients to be positive.
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(:issue:`5131`)
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- Added the ``X_norm_squared`` parameter to :func:`metrics.pairwise.euclidean_distances`
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to provide precomputed squared norms for ``X``.
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- Added the ``fit_predict`` method to :class:`pipeline.Pipeline`.
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- Added the :func:`preprocessing.minmax_scale` function.
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Bug fixes
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.........
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- Fixed non-determinism in :class:`dummy.DummyClassifier` with sparse
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multi-label output. By `Andreas Müller`_.
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- Fixed the output shape of :class:`linear_model.RANSACRegressor` to
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``(n_samples, )``. By `Andreas Müller`_.
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- Fixed bug in `decomposition.DictLearning` when ``n_jobs < 0``. By
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`Andreas Müller`_.
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- Fixed bug where `grid_search.RandomizedSearchCV` could consume a
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lot of memory for large discrete grids. By `Joel Nothman`_.
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- Fixed bug in :class:`linear_model.LogisticRegressionCV` where `penalty` was ignored
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in the final fit. By `Manoj Kumar`_.
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- Fixed bug in `ensemble.forest.ForestClassifier` while computing
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oob_score and X is a sparse.csc_matrix. By :user:`Ankur Ankan <ankurankan>`.
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- All regressors now consistently handle and warn when given ``y`` that is of
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shape ``(n_samples, 1)``. By `Andreas Müller`_ and Henry Lin.
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(:issue:`5431`)
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- Fix in :class:`cluster.KMeans` cluster reassignment for sparse input by
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`Lars Buitinck`_.
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- Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis` that
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could cause asymmetric covariance matrices when using shrinkage. By `Martin
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Billinger`_.
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- Fixed `cross_validation.cross_val_predict` for estimators with
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sparse predictions. By Buddha Prakash.
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- Fixed the ``predict_proba`` method of :class:`linear_model.LogisticRegression`
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to use soft-max instead of one-vs-rest normalization. By `Manoj Kumar`_.
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(:issue:`5182`)
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- Fixed the `partial_fit` method of :class:`linear_model.SGDClassifier`
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when called with ``average=True``. By :user:`Andrew Lamb <andylamb>`.
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(:issue:`5282`)
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- Dataset fetchers use different filenames under Python 2 and Python 3 to
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avoid pickling compatibility issues. By `Olivier Grisel`_.
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(:issue:`5355`)
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- Fixed a bug in :class:`naive_bayes.GaussianNB` which caused classification
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results to depend on scale. By `Jake Vanderplas`_.
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- Fixed temporarily :class:`linear_model.Ridge`, which was incorrect
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when fitting the intercept in the case of sparse data. The fix
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automatically changes the solver to 'sag' in this case.
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:issue:`5360` by `Tom Dupre la Tour`_.
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- Fixed a performance bug in `decomposition.RandomizedPCA` on data
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with a large number of features and fewer samples. (:issue:`4478`)
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By `Andreas Müller`_, `Loic Esteve`_ and :user:`Giorgio Patrini <giorgiop>`.
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- Fixed bug in `cross_decomposition.PLS` that yielded unstable and
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platform dependent output, and failed on `fit_transform`.
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By :user:`Arthur Mensch <arthurmensch>`.
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- Fixes to the ``Bunch`` class used to store datasets.
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- Fixed `ensemble.plot_partial_dependence` ignoring the
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``percentiles`` parameter.
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- Providing a ``set`` as vocabulary in ``CountVectorizer`` no longer
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leads to inconsistent results when pickling.
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- Fixed the conditions on when a precomputed Gram matrix needs to
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be recomputed in :class:`linear_model.LinearRegression`,
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:class:`linear_model.OrthogonalMatchingPursuit`,
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:class:`linear_model.Lasso` and :class:`linear_model.ElasticNet`.
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- Fixed inconsistent memory layout in the coordinate descent solver
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that affected `linear_model.DictionaryLearning` and
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`covariance.GraphLasso`. (:issue:`5337`)
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By `Olivier Grisel`_.
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- :class:`manifold.LocallyLinearEmbedding` no longer ignores the ``reg``
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parameter.
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- Nearest Neighbor estimators with custom distance metrics can now be pickled.
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(:issue:`4362`)
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- Fixed a bug in :class:`pipeline.FeatureUnion` where ``transformer_weights``
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were not properly handled when performing grid-searches.
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- Fixed a bug in :class:`linear_model.LogisticRegression` and
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:class:`linear_model.LogisticRegressionCV` when using
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``class_weight='balanced'`` or ``class_weight='auto'``.
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By `Tom Dupre la Tour`_.
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- Fixed bug :issue:`5495` when
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doing OVR(SVC(decision_function_shape="ovr")). Fixed by
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:user:`Elvis Dohmatob <dohmatob>`.
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API changes summary
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-------------------
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- Attribute `data_min`, `data_max` and `data_range` in
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:class:`preprocessing.MinMaxScaler` are deprecated and won't be available
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from 0.19. Instead, the class now exposes `data_min_`, `data_max_`
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and `data_range_`. By :user:`Giorgio Patrini <giorgiop>`.
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- All Scaler classes now have an `scale_` attribute, the feature-wise
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rescaling applied by their `transform` methods. The old attribute `std_`
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in :class:`preprocessing.StandardScaler` is deprecated and superseded
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by `scale_`; it won't be available in 0.19. By :user:`Giorgio Patrini <giorgiop>`.
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- :class:`svm.SVC` and :class:`svm.NuSVC` now have an ``decision_function_shape``
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parameter to make their decision function of shape ``(n_samples, n_classes)``
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by setting ``decision_function_shape='ovr'``. This will be the default behavior
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starting in 0.19. By `Andreas Müller`_.
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- Passing 1D data arrays as input to estimators is now deprecated as it
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caused confusion in how the array elements should be interpreted
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as features or as samples. All data arrays are now expected
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to be explicitly shaped ``(n_samples, n_features)``.
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By :user:`Vighnesh Birodkar <vighneshbirodkar>`.
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- `lda.LDA` and `qda.QDA` have been moved to
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:class:`discriminant_analysis.LinearDiscriminantAnalysis` and
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:class:`discriminant_analysis.QuadraticDiscriminantAnalysis`.
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- The ``store_covariance`` and ``tol`` parameters have been moved from
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the fit method to the constructor in
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:class:`discriminant_analysis.LinearDiscriminantAnalysis` and the
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``store_covariances`` and ``tol`` parameters have been moved from the
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fit method to the constructor in
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:class:`discriminant_analysis.QuadraticDiscriminantAnalysis`.
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- Models inheriting from ``_LearntSelectorMixin`` will no longer support the
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transform methods. (i.e, RandomForests, GradientBoosting, LogisticRegression,
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DecisionTrees, SVMs and SGD related models). Wrap these models around the
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metatransfomer :class:`feature_selection.SelectFromModel` to remove
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features (according to `coefs_` or `feature_importances_`)
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which are below a certain threshold value instead.
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- :class:`cluster.KMeans` re-runs cluster-assignments in case of non-convergence,
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to ensure consistency of ``predict(X)`` and ``labels_``. By
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:user:`Vighnesh Birodkar <vighneshbirodkar>`.
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- Classifier and Regressor models are now tagged as such using the
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``_estimator_type`` attribute.
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- Cross-validation iterators always provide indices into training and test set,
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not boolean masks.
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- The ``decision_function`` on all regressors was deprecated and will be
|
|
removed in 0.19. Use ``predict`` instead.
|
|
|
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- `datasets.load_lfw_pairs` is deprecated and will be removed in 0.19.
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Use :func:`datasets.fetch_lfw_pairs` instead.
|
|
|
|
- The deprecated ``hmm`` module was removed.
|
|
|
|
- The deprecated ``Bootstrap`` cross-validation iterator was removed.
|
|
|
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- The deprecated ``Ward`` and ``WardAgglomerative`` classes have been removed.
|
|
Use :class:`cluster.AgglomerativeClustering` instead.
|
|
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- `cross_validation.check_cv` is now a public function.
|
|
|
|
- The property ``residues_`` of :class:`linear_model.LinearRegression` is deprecated
|
|
and will be removed in 0.19.
|
|
|
|
- The deprecated ``n_jobs`` parameter of :class:`linear_model.LinearRegression` has been moved
|
|
to the constructor.
|
|
|
|
- Removed deprecated ``class_weight`` parameter from :class:`linear_model.SGDClassifier`'s ``fit``
|
|
method. Use the construction parameter instead.
|
|
|
|
- The deprecated support for the sequence of sequences (or list of lists) multilabel
|
|
format was removed. To convert to and from the supported binary
|
|
indicator matrix format, use
|
|
:class:`MultiLabelBinarizer <preprocessing.MultiLabelBinarizer>`.
|
|
|
|
- The behavior of calling the ``inverse_transform`` method of ``Pipeline.pipeline`` will
|
|
change in 0.19. It will no longer reshape one-dimensional input to two-dimensional input.
|
|
|
|
- The deprecated attributes ``indicator_matrix_``, ``multilabel_`` and ``classes_`` of
|
|
:class:`preprocessing.LabelBinarizer` were removed.
|
|
|
|
- Using ``gamma=0`` in :class:`svm.SVC` and :class:`svm.SVR` to automatically set the
|
|
gamma to ``1. / n_features`` is deprecated and will be removed in 0.19.
|
|
Use ``gamma="auto"`` instead.
|
|
|
|
Code Contributors
|
|
-----------------
|
|
Aaron Schumacher, Adithya Ganesh, akitty, Alexandre Gramfort, Alexey Grigorev,
|
|
Ali Baharev, Allen Riddell, Ando Saabas, Andreas Mueller, Andrew Lamb, Anish
|
|
Shah, Ankur Ankan, Anthony Erlinger, Ari Rouvinen, Arnaud Joly, Arnaud Rachez,
|
|
Arthur Mensch, banilo, Barmaley.exe, benjaminirving, Boyuan Deng, Brett Naul,
|
|
Brian McFee, Buddha Prakash, Chi Zhang, Chih-Wei Chang, Christof Angermueller,
|
|
Christoph Gohlke, Christophe Bourguignat, Christopher Erick Moody, Chyi-Kwei
|
|
Yau, Cindy Sridharan, CJ Carey, Clyde-fare, Cory Lorenz, Dan Blanchard, Daniel
|
|
Galvez, Daniel Kronovet, Danny Sullivan, Data1010, David, David D Lowe, David
|
|
Dotson, djipey, Dmitry Spikhalskiy, Donne Martin, Dougal J. Sutherland, Dougal
|
|
Sutherland, edson duarte, Eduardo Caro, Eric Larson, Eric Martin, Erich
|
|
Schubert, Fernando Carrillo, Frank C. Eckert, Frank Zalkow, Gael Varoquaux,
|
|
Ganiev Ibraim, Gilles Louppe, Giorgio Patrini, giorgiop, Graham Clenaghan,
|
|
Gryllos Prokopis, gwulfs, Henry Lin, Hsuan-Tien Lin, Immanuel Bayer, Ishank
|
|
Gulati, Jack Martin, Jacob Schreiber, Jaidev Deshpande, Jake Vanderplas, Jan
|
|
Hendrik Metzen, Jean Kossaifi, Jeffrey04, Jeremy, jfraj, Jiali Mei,
|
|
Joe Jevnik, Joel Nothman, John Kirkham, John Wittenauer, Joseph, Joshua Loyal,
|
|
Jungkook Park, KamalakerDadi, Kashif Rasul, Keith Goodman, Kian Ho, Konstantin
|
|
Shmelkov, Kyler Brown, Lars Buitinck, Lilian Besson, Loic Esteve, Louis Tiao,
|
|
maheshakya, Maheshakya Wijewardena, Manoj Kumar, MarkTab marktab.net, Martin
|
|
Ku, Martin Spacek, MartinBpr, martinosorb, MaryanMorel, Masafumi Oyamada,
|
|
Mathieu Blondel, Matt Krump, Matti Lyra, Maxim Kolganov, mbillinger, mhg,
|
|
Michael Heilman, Michael Patterson, Miroslav Batchkarov, Nelle Varoquaux,
|
|
Nicolas, Nikolay Mayorov, Olivier Grisel, Omer Katz, Óscar Nájera, Pauli
|
|
Virtanen, Peter Fischer, Peter Prettenhofer, Phil Roth, pianomania, Preston
|
|
Parry, Raghav RV, Rob Zinkov, Robert Layton, Rohan Ramanath, Saket Choudhary,
|
|
Sam Zhang, santi, saurabh.bansod, scls19fr, Sebastian Raschka, Sebastian
|
|
Saeger, Shivan Sornarajah, SimonPL, sinhrks, Skipper Seabold, Sonny Hu, sseg,
|
|
Stephen Hoover, Steven De Gryze, Steven Seguin, Theodore Vasiloudis, Thomas
|
|
Unterthiner, Tiago Freitas Pereira, Tian Wang, Tim Head, Timothy Hopper,
|
|
tokoroten, Tom Dupré la Tour, Trevor Stephens, Valentin Stolbunov, Vighnesh
|
|
Birodkar, Vinayak Mehta, Vincent, Vincent Michel, vstolbunov, wangz10, Wei Xue,
|
|
Yucheng Low, Yury Zhauniarovich, Zac Stewart, zhai_pro, Zichen Wang
|