ENH add np.nan option for zero_division in precision/recall/f-score (scikit-learn#25531)

Co-authored-by: Guillaume Lemaitre <[email protected]>

Author: marctorsoc

doc/whats_new/v1.3.rst

@@ -163,7 +163,7 @@ Changelog
 
 - |API| The `sample_weight` parameter in `predict` for
   :meth:`cluster.KMeans.predict` and :meth:`cluster.MiniBatchKMeans.predict`
-  is now deprecated and will be removed in v1.5. 
+  is now deprecated and will be removed in v1.5.
   :pr:`25251` by :user:`Gleb Levitski <glevv>`.
 
 - |Enhancement| The `sample_weight` parameter now will be used in centroids
@@ -279,9 +279,23 @@ Changelog
   :user:`Guillaume Lemaitre <glemaitre>`, :user:`Omar Salman <OmarManzoor>` and
   :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
+- |Feature| Adds `zero_division=np.nan` to multiple classification metrics:
+  :func:`precision_score`, :func:`recall_score`, :func:`f1_score`,
+  :func:`fbeta_score`, :func:`precision_recall_fscore_support`,
+  :func:`classification_report`. When `zero_division=np.nan` and there is a
+  zero division, the metric is undefined and is excluded from averaging. When not used
+  for averages, the value returned is `np.nan`.
+  :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.
+
 - |Fix| :func:`metric.manhattan_distances` now supports readonly sparse datasets.
   :pr:`25432` by :user:`Julien Jerphanion <jjerphan>`.
 
+- |Fix| Fixed :func:`classification_report` so that empty input will return
+  `np.nan`. Previously, "macro avg" and `weighted avg` would return
+  e.g. `f1-score=np.nan` and `f1-score=0.0`, being inconsistent. Now, they
+  both return `np.nan`.
+  :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.
+
 - |Enhancement| :class:`metrics.silhouette_samples` nows accepts a sparse
   matrix of pairwise distances between samples, or a feature array.
   :pr:`18723` by :user:`Sahil Gupta <sahilgupta2105>` and