DOC update roadmap
@@ -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`
@@ -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
@@ -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`
@@ -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`
-
