[DOC] Mention classification type in plot_haxby_glm_decoding.py (#4122)

* Mention classification type in plot_haxby_glm_decoding.py

Addresses issue: #4109

* Update examples/02_decoding/plot_haxby_glm_decoding.py

Co-authored-by: Yasmin <63292494+ymzayek@users.noreply.github.com>

* Update examples/02_decoding/plot_haxby_glm_decoding.py

Co-authored-by: Yasmin <63292494+ymzayek@users.noreply.github.com>

* Update latest.rst

* Update CITATION.cff

* Update doc/changes/latest.rst

---------

Co-authored-by: Yasmin <63292494+ymzayek@users.noreply.github.com>

CITATION.cff

@@ -701,3 +701,7 @@ authors:
     family-names: Nájera
     website: https://github.com/Titan-C
     affiliation: Checkmk
+  - given-names: Tamer
+    family-names: Gezici
+    website: https://github.com/TamerGezici
+    affiliation: Bilkent University

doc/changes/latest.rst

@@ -28,6 +28,7 @@ Enhancements
 - :bdg-primary:`Code` Add ``two_sided`` option for :class:`~nilearn.image.binarize_img` (:gh:`4121` by `Steven Meisler`_).
 - :bdg-dark:`Code` :meth:`~maskers.NiftiLabelsMasker.generate_report` now uses appropriate cut coordinates when functional image is provided (:gh:`4099` by `Yasmin Mzayek`_ and `Nicolas Gensollen`_).
 - :bdg-info:`Plotting` When plotting thresholded statistical maps with a colorbar, the threshold value(s) will now be displayed as tick labels on the colorbar (:gh:`#2833` by `Nicolas Gensollen`_).
+- :bdg-primary:`Doc`  Mention the classification type (all-vs-one) in  :ref:`sphx_glr_auto_examples_02_decoding_plot_haxby_glm_decoding.py` (:gh:`4122` by `Tamer Gezici`_). 
 
 Changes
 -------

examples/02_decoding/plot_haxby_glm_decoding.py

@@ -3,7 +3,8 @@
 =========================================================
 
 Full step-by-step example of fitting a :term:`GLM`
-to perform a decoding experiment.
+to perform a decoding experiment. In this decoding analysis,
+we will be doing a one-vs-all classification.
 We use the data from one subject of the Haxby dataset.
 
 More specifically:
@@ -177,6 +178,7 @@
 decoder.fit(z_maps, conditions_label, groups=session_label)
 
 # Return the corresponding mean prediction accuracy compared to chance
+# for classifying one-vs-all items.
 
 classification_accuracy = np.mean(list(decoder.cv_scores_.values()))
 chance_level = 1.0 / len(np.unique(conditions))