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May 9, 2019
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[MRG] Update roadmap #13809

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19 changes: 0 additions & 19 deletions doc/roadmap.rst
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Expand Up @@ -128,7 +128,6 @@ bottom.

#. Improved tools for model diagnostics and basic inference

* partial dependence plots :issue:`5653`
* alternative feature importances implementations (e.g. methods or wrappers)
* better ways to handle validation sets when fitting
* better ways to find thresholds / create decision rules :issue:`8614`
Expand All @@ -144,19 +143,6 @@ bottom.
:issue:`6929`
* Callbacks or a similar system would facilitate logging and early stopping

#. Use scipy BLAS Cython bindings

* This will make it possible to get rid of our partial copy of suboptimal
Atlas C-routines. :issue:`11638`
* This should speed up the Windows and Linux wheels

#. Allow fine-grained parallelism in cython

* Now that we do not use fork-based multiprocessing in joblib anymore it's
possible to use the prange / openmp thread management which makes it
possible to have very efficient thread-based parallelism at the Cython
level. Example with K-Means: :issue:`11950`

#. Distributed parallelism

* Joblib can now plug onto several backends, some of them can distribute the
Expand Down Expand Up @@ -240,9 +226,6 @@ Subpackage-specific goals
:mod:`sklearn.ensemble`

* a stacking implementation
* a binned feature histogram based and thread parallel implementation of
decision trees to compete with the performance of state of the art gradient
boosting like LightGBM.

:mod:`sklearn.model_selection`

Expand All @@ -269,5 +252,3 @@ Subpackage-specific goals

* Performance issues with `Pipeline.memory`
* see "Everything in Scikit-learn should conform to our API contract" above
* Add a verbose option :issue:`10435`