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Copy file name to clipboardExpand all lines: doc/whats_new.rst
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@@ -22,6 +22,15 @@ New features
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:class:`feature_selection.SelectPercentile` as score functions.
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By `Andrea Bravi`_ and `Nikolay Mayorov`_.
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- Class :class:`decomposition.RandomizedPCA` is now factored into :class:`decomposition.PCA`
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and it is available calling with parameter ``svd_solver='randomized'``.
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The default number of ``n_iter`` for ``'randomized'`` has changed to 4. The old
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behavior of PCA is recovered by ``svd_solver='full'``. An additional solver
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calls `arpack` and performs truncated (non-randomized) SVD. By default,
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the best solver is selected depending on the size of the input and the
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number of components requested.
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(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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- The Gaussian Process module has been reimplemented and now offers classification
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and regression estimators through :class:`gaussian_process.GaussianProcessClassifier`
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and :class:`gaussian_process.GaussianProcessRegressor`. Among other things, the new
@@ -114,17 +123,26 @@ Bug fixes
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- :class:`StratifiedKFold` now raises error if all n_labels for individual classes is less than n_folds.
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(`#6182 <https://github.com/scikit-learn/scikit-learn/pull/6182>`_) by `Devashish Deshpande`_.
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- :class:`RandomizedPCA` default number of `iterated_power` is 2 instead of 3.
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This is a speed up with a minor precision decrease. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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- :class:`RandomizedPCA` default number of `iterated_power` is 4 instead of 3.
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(`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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- :func:`randomized_svd` performs 2 power iterations by default, instead or 0.
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In practice this is often enough for obtaining a good approximation of the
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true eigenvalues/vectors in the presence of noise. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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- :func:`utils.extmath.randomized_svd` performs 4 power iterations by default, instead or 0.
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In practice this is enough for obtaining a good approximation of the
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true eigenvalues/vectors in the presence of noise. When `n_components` is
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small (< .1 * min(X.shape)) `n_iter` is set to 7, unless the user specifies
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a higher number. This improves precision with few components.
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(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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- :func:`randomized_range_finder` is more numerically stable when many
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- :func:`utils.extmath.randomized_range_finder` is more numerically stable when many
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power iterations are requested, since it applies LU normalization by default.
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If `n_iter<2` numerical issues are unlikely, thus no normalization is applied.
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Other normalization options are available: 'none', 'LU' and 'QR'. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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Other normalization options are available: 'none', 'LU' and 'QR'.
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(`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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- Whiten/non-whiten inconsistency between components of :class:`decomposition.PCA`
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and :class:`decomposition.RandomizedPCA` (now factored into PCA, see the
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New features) is fixed. `components_` are stored with no whitening.
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(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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- Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized
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Laplacian matrix was incorrectly set to 1. (`#4995 <https://github.com/scikit-learn/scikit-learn/pull/4995>`_) By `Peter Fischer`_.
@@ -213,7 +231,8 @@ Changelog
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New features
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............
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- All the Scaler classes but :class:`RobustScaler` can be fitted online by
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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 `Giorgio Patrini`_.
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- The new class :class:`ensemble.VotingClassifier` implements a
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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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