rename title that where too long

Author: glemaitre

doc/references/utils.rst

@@ -7,10 +7,29 @@ Utilities
 
 .. currentmodule:: imblearn.utils
 
+Validation checks used in samplers
+----------------------------------
+
 .. autosummary::
    :toctree: generated/
    :template: function.rst
 
    estimator_checks.parametrize_with_checks
    check_neighbors_object
    check_sampling_strategy
+   check_target_type
+
+Testing compatibility of your own sampler
+-----------------------------------------
+
+.. automodule:: imblearn.utils.estimator_checks
+    :no-members:
+    :no-inherited-members:
+
+.. currentmodule:: imblearn.utils.estimator_checks
+
+.. autosummary::
+   :toctree: generated/
+   :template: function.rst
+
+   parametrize_with_checks

examples/api/plot_sampling_strategy_usage.py

@@ -1,7 +1,7 @@
 """
-=========================================================================
-Usage of the ``sampling_strategy`` parameter for the different algorithms
-=========================================================================
+====================================================
+How to use ``sampling_strategy`` in imbalanced-learn
+====================================================
 
 This example shows the different usage of the parameter ``sampling_strategy``
 for the different family of samplers (i.e. over-sampling, under-sampling. or

examples/combine/plot_comparison_combine.py

@@ -1,7 +1,7 @@
 """
-====================================================================
-Comparison of the combination of over- and under-sampling algorithms
-====================================================================
+==================================================
+Compare sampler combining over- and under-sampling
+==================================================
 
 This example shows the effect of applying an under-sampling algorithms after
 SMOTE over-sampling. In the literature, Tomek's link and edited nearest

examples/ensemble/plot_comparison_ensemble_classifier.py

@@ -1,7 +1,7 @@
 """
-==============================================================
-Comparison of ensembling classifiers internally using sampling
-==============================================================
+=============================================
+Compare ensemble classifiers using resampling
+=============================================
 
 Ensembling classifiers have shown to improve classification performance compare
 to single learner. However, they will be affected by class imbalance. This

examples/over-sampling/plot_comparison_over_sampling.py

@@ -1,7 +1,7 @@
 """
-====================================================
-Comparison of the different over-sampling algorithms
-====================================================
+==============================
+Compare over-sampling samplers
+==============================
 
 The following example attends to make a qualitative comparison between the
 different over-sampling algorithms available in the imbalanced-learn package.

examples/over-sampling/plot_illustration_generation_sample.py

@@ -1,7 +1,7 @@
 """
-====================================================================
-Illustration of the sample generation in the over-sampling algorithm
-====================================================================
+============================================
+Sample generator used in SMOTE-like samplers
+============================================
 
 This example illustrates how a new sample is generated taking into account the
 neighbourhood of this sample. A new sample is generated by selecting the

examples/under-sampling/plot_comparison_under_sampling.py

@@ -1,7 +1,7 @@
 """
-=====================================================
-Comparison of the different under-sampling algorithms
-=====================================================
+===============================
+Compare under-sampling samplers
+===============================
 
 The following example attends to make a qualitative comparison between the
 different under-sampling algorithms available in the imbalanced-learn package.

examples/under-sampling/plot_illustration_nearmiss.py

@@ -1,7 +1,7 @@
 """
-==========================================================================
-Illustration of the sample selection for the different NearMiss algorithms
-==========================================================================
+============================
+Sample selection in NearMiss
+============================
 
 This example illustrates the different way of selecting example in NearMiss.