DOC: Minor revision to the API Reference cross-links (#32166)

star1327p · jeremiedbb · web-flow · commit d13cda7a9095 · 2025-09-12T14:39:28.000+02:00
Co-authored-by: Jérémie du Boisberranger &lt;jeremie@probabl.ai&gt;
diff --git a/sklearn/datasets/_samples_generator.py b/sklearn/datasets/_samples_generator.py
@@ -1862,7 +1862,7 @@ def make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None, hole=False):
 
     Read more in the :ref:`User Guide <sample_generators>`.
 
-    Adapted with permission from Stephen Marsland's code [1].
+    Adapted with permission from Stephen Marsland's code [1]_.
 
     Parameters
     ----------
@@ -1891,7 +1891,7 @@ def make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None, hole=False):
 
     Notes
     -----
-    The algorithm is from Marsland [1].
+    The algorithm is from Marsland [1]_.
 
     References
     ----------
@@ -2058,11 +2058,13 @@ def make_gaussian_quantiles(
 
     Notes
     -----
-    The dataset is from Zhu et al [1].
+    The dataset is from Zhu et al [1]_.
 
     References
     ----------
-    .. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
+    .. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost."
+           Statistics and its Interface 2.3 (2009): 349-360.
+           <10.4310/SII.2009.v2.n3.a8>`
 
     Examples
     --------
diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
@@ -1479,7 +1479,8 @@ class labels (multi-output problem).
 
     References
     ----------
-    .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+    .. [1] :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+           <10.1023/A:1010933404324>`
 
     Examples
     --------
@@ -1852,11 +1853,12 @@ class RandomForestRegressor(ForestRegressor):
 
     The default value ``max_features=1.0`` uses ``n_features``
     rather than ``n_features / 3``. The latter was originally suggested in
-    [1], whereas the former was more recently justified empirically in [2].
+    [1]_, whereas the former was more recently justified empirically in [2]_.
 
     References
     ----------
-    .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+    .. [1] :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+           <10.1023/A:1010933404324>`
 
     .. [2] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
            trees", Machine Learning, 63(1), 3-42, 2006.
@@ -2842,7 +2844,7 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):
            Machine Learning, 63(1), 3-42, 2006.
     .. [2] Moosmann, F. and Triggs, B. and Jurie, F.  "Fast discriminative
            visual codebooks using randomized clustering forests"
-           NIPS 2007
+           NIPS 2007.
 
     Examples
     --------
diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
@@ -487,7 +487,7 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
     tol : float, default=1e-6
         The precision of the solution (`coef_`) is determined by `tol` which
         specifies a different convergence criterion for the `lsqr` solver.
-        `tol` is set as `atol` and `btol` of `scipy.sparse.linalg.lsqr` when
+        `tol` is set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when
         fitting on sparse training data. This parameter has no effect when fitting
         on dense data.
 
@@ -554,8 +554,8 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
     Notes
     -----
     From the implementation point of view, this is just plain Ordinary
-    Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares
-    (scipy.optimize.nnls) wrapped as a predictor object.
+    Least Squares (:func:`scipy.linalg.lstsq`) or Non Negative Least Squares
+    (:func:`scipy.optimize.nnls`) wrapped as a predictor object.
 
     Examples
     --------
diff --git a/sklearn/utils/_show_versions.py b/sklearn/utils/_show_versions.py
@@ -75,7 +75,7 @@ def _get_deps_info():
 
 
 def show_versions():
-    """Print useful debugging information"
+    """Print useful debugging information.
 
     .. versionadded:: 0.20
 
