Pushing the docs to dev/ for branch: main, commit c8a5f807a365a069b4d96da1a84318ce6253eab7

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 15f3f7f61422aa3be0c233c0cff60912
+config: 28e0303feb83fffc87fce6385db52319
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.18"
+      "version": "3.11.14"
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dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -5,7 +5,7 @@
 This shows an example of a neighbors-based query (in particular a kernel
 density estimate) on geospatial data, using a Ball Tree built upon the
 Haversine distance metric -- i.e. distances over points in latitude/longitude.
-The dataset is provided by Phillips et. al. (2006).
+The dataset is provided by Phillips et. al. (2006) [1]_.
 If available, the example uses
 `basemap <https://matplotlib.org/basemap/>`_
 to plot the coast lines and national boundaries of South America.
@@ -29,10 +29,10 @@
 References
 ----------
 
-- `"Maximum entropy modeling of species geographic distributions"
-  <http://rob.schapire.net/papers/ecolmod.pdf>`_
-  S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
-  190:231-259, 2006.
+.. [1] `"Maximum entropy modeling of species geographic distributions"
+       <http://rob.schapire.net/papers/ecolmod.pdf>`_
+       S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
+       190:231-259, 2006.
 """
 
 # Authors: The scikit-learn developers

dev/_downloads/02a1306a494b46cc56c930ceec6e8c4a/plot_species_kde.py

@@ -5,7 +5,7 @@
 
 This example demonstrates how to balance model complexity and cross-validated score by
 finding a decent accuracy within 1 standard deviation of the best accuracy score while
-minimising the number of :class:`~sklearn.decomposition.PCA` components [1]. It uses
+minimising the number of :class:`~sklearn.decomposition.PCA` components [1]_. It uses
 :class:`~sklearn.model_selection.GridSearchCV` with a custom refit callable to select
 the optimal model.
 
@@ -14,9 +14,11 @@
 which falls into the range within 1 standard deviation of the best accuracy
 score.
 
-[1] Hastie, T., Tibshirani, R.,, Friedman, J. (2001). Model Assessment and
-Selection. The Elements of Statistical Learning (pp. 219-260). New York,
-NY, USA: Springer New York Inc..
+References
+----------
+.. [1] Hastie, T., Tibshirani, R., Friedman, J. (2001). Model Assessment and
+   Selection. The Elements of Statistical Learning (pp. 219-260). New York,
+   NY, USA: Springer New York Inc.
 """
 
 # Authors: The scikit-learn developers
@@ -47,10 +49,12 @@
 # ----------------
 #
 # We define two helper functions:
+#
 # 1. `lower_bound`: Calculates the threshold for acceptable performance
-# (best score - 1 std)
+#    (best score - 1 std)
+#
 # 2. `best_low_complexity`: Selects the model with the fewest PCA components that
-# exceeds this threshold
+#    exceeds this threshold
 
 
 def lower_bound(cv_results):
@@ -106,7 +110,9 @@ def best_low_complexity(cv_results):
 # --------------------------------------
 #
 # We create a pipeline with two steps:
+#
 # 1. Dimensionality reduction using PCA
+#
 # 2. Classification using LogisticRegression
 #
 # We'll search over different numbers of PCA components to find the optimal complexity.
@@ -367,9 +373,12 @@ def best_low_complexity(cv_results):
 # callable with :class:`~sklearn.model_selection.GridSearchCV`.
 #
 # Key takeaways:
+#
 # 1. The one-standard-error rule provides a good rule of thumb to select simpler models
+#
 # 2. Custom refit callables in :class:`~sklearn.model_selection.GridSearchCV` allow for
-# flexible model selection strategies
+#    flexible model selection strategies
+#
 # 3. Visualizing both train and test scores helps identify potential overfitting
 #
 # This approach can be applied to other model selection scenarios where balancing

dev/_downloads/02d88d76c60b7397c8c6e221b31568dd/plot_grid_search_refit_callable.py

@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.18"
+      "version": "3.11.14"
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