DOC plot classification probability (scikit-learn#29921)

Co-authored-by: adrinjalali <adrin.jalali@gmail.com>

Author: Sean-Jay-M

doc/modules/linear_model.rst

@@ -891,6 +891,7 @@ regularization.
 * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`
 * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py`
 * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py`
+* :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`
 
 Binary Case
 -----------

doc/modules/multiclass.rst

@@ -228,6 +228,7 @@ in which cell [i, j] indicates the presence of label j in sample i.
 .. rubric:: Examples
 
 * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
+* :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`
 
 .. _ovo_classification:
 

doc/modules/svm.rst

@@ -112,6 +112,7 @@ properties of these support vectors can be found in attributes
 
 * :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py`
 * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
+* :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`
 
 .. _svm_multi_class:
 

sklearn/gaussian_process/_gpc.py

@@ -641,6 +641,9 @@ def optimizer(obj_func, initial_theta, bounds):
     >>> gpc.predict_proba(X[:2,:])
     array([[0.83548752, 0.03228706, 0.13222543],
            [0.79064206, 0.06525643, 0.14410151]])
+
+    For a comaprison of the GaussianProcessClassifier with other classifiers see:
+    :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
     """
 
     _parameter_constraints: dict = {

sklearn/linear_model/_logistic.py

@@ -1095,6 +1095,9 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
            [9.7...e-01, 2.8...e-02, ...e-08]])
     >>> clf.score(X, y)
     0.97...
+
+    For a comaprison of the LogisticRegression with other classifiers see:
+    :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
     """
 
     _parameter_constraints: dict = {

sklearn/svm/_classes.py

@@ -853,6 +853,9 @@ class SVC(BaseSVC):
 
     >>> print(clf.predict([[-0.8, -1]]))
     [1]
+
+    For a comaprison of the SVC with other classifiers see:
+    :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
     """
 
     _impl = "c_svc"