changes as suggested in review

imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py

@@ -110,19 +110,18 @@ class CondensedNearestNeighbour(BaseCleaningSampler):
  Examples
  --------
  >>> from collections import Counter # doctest: +SKIP
- >>> from sklearn.datasets import make_classification # doctest: +SKIP
+ >>> from sklearn.datasets import fetch_openml # doctest: +SKIP
+ >>> from sklearn.preprocessing import scale # doctest: +SKIP
  >>> from imblearn.under_sampling import \
 CondensedNearestNeighbour # doctest: +SKIP
- >>> X, y = make_classification(n_classes=2, class_sep=2, \
-weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, \
-n_features=20, n_clusters_per_class=1, n_samples=1000, \
-random_state=10) # doctest: +SKIP
+ >>> X, y = fetch_openml('diabetes', version=1, return_X_y=True) # doctest: +SKIP
+ >>> X = scale(X) # doctest: +SKIP
  >>> print('Original dataset shape %s' % Counter(y)) # doctest: +SKIP
- Original dataset shape Counter({{1: 900, 0: 100}}) # doctest: +SKIP
+ Original dataset shape Counter({{1: 500, 0: 268}}) # doctest: +SKIP
  >>> cnn = CondensedNearestNeighbour(random_state=42) # doctest: +SKIP
  >>> X_res, y_res = cnn.fit_resample(X, y) #doctest: +SKIP
  >>> print('Resampled dataset shape %s' % Counter(y_res)) # doctest: +SKIP
- Resampled dataset shape Counter({{0: 100, 1: 44}}) # doctest: +SKIP
+ Resampled dataset shape Counter({{0: 268, 1: 181}}) # doctest: +SKIP
  """
 
  _parameter_constraints: dict = {