What's new: group entries by topic

Author: glouppe

doc/whats_new.rst

@@ -20,23 +20,14 @@ Changelog
      :class:`ensemble.BaggingRegressor` meta-estimators for ensembling
      any kind of base estimator. See the :ref:`Bagging <bagging>` section of
      the user guide for details and examples. By `Gilles Louppe`_.
-
-   - Speed improvement of the :mod:`sklearn.ensemble.gradient_boosting` module.
-     By `Gilles Louppe`_ and `Peter Prettenhofer`_.
 
-   - Memory improvements of extra trees and random forest by
-     `Arnaud Joly`_.
+   - Memory improvements of decision trees, by `Arnaud Joly`_.
 
-   - Reduce memory usage and overhead when fitting and predicting with forests
-     of randomized trees in parallel with ``n_jobs != 1`` by leveraging new
-     threading backend of joblib 0.8 and releasing the GIL in the tree fitting
-     Cython code.  By `Olivier Grisel`_ and `Gilles Louppe`_.
-
    - Decision trees can now be built in best-first manner by using ``max_leaf_nodes``
      as the stopping criteria. Refactored the tree code to use either a
      stack or a priority queue for tree building.
      By `Peter Prettenhofer`_ and `Gilles Louppe`_.
-
+     
    - Decision trees can now be fitted on fortran- and c-style arrays, and
      non-continuous arrays without the need to make a copy.
      If the input array has a different dtype than ``np.float32``, a fortran-
@@ -51,13 +42,24 @@ Changelog
    - Changed the internal storage of decision trees to use a struct array.
      This fixed some small bugs, while improving code and providing a small
      speed gain. By `Joel Nothman`_.
+     
+   - Reduce memory usage and overhead when fitting and predicting with forests
+     of randomized trees in parallel with ``n_jobs != 1`` by leveraging new
+     threading backend of joblib 0.8 and releasing the GIL in the tree fitting
+     Cython code.  By `Olivier Grisel`_ and `Gilles Louppe`_.
 
+   - Speed improvement of the :mod:`sklearn.ensemble.gradient_boosting` module.
+     By `Gilles Louppe`_ and `Peter Prettenhofer`_.
+     
    - Various enhancements to the  :mod:`sklearn.ensemble.gradient_boosting`
      module: a ``warm_start`` argument to fit additional trees,
      a ``max_leaf_nodes`` argument to fit GBM style trees,
      a ``monitor`` fit argument to inspect the estimator during training, and
      refactoring of the verbose code. By `Peter Prettenhofer`_.
 
+   - Fixed bug in :class:`gradient_boosting.GradientBoostingRegressor` with
+     ``loss='huber'``: ``gamma`` might have not been initialized.
+     
    - Fixed feature importances as computed with a forest of randomized trees
      when fit with ``sample_weight != None`` and/or with ``bootstrap=True``.
      By `Gilles Louppe`_.
@@ -110,9 +112,6 @@ Changelog
      and predictive power.
      By `Eustache Diemert`_.
 
-   - Fixed bug in :class:`gradient_boosting.GradientBoostingRegressor` with
-     ``loss='huber'``: ``gamma`` might have not been initialized.
-
    - :class:`dummy.DummyClassifier` can now be used to predict a constant
      output value. By `Manoj Kumar`_.