MNT adapt for scikit-learn 1.1

[glemaitre]

Check and correct the code for scikit-learn 1.1

[codecov]

Codecov Report

Merging #902 (adffd69) into master (d610d91) will decrease coverage by 2.70%.
The diff coverage is 87.71%.

@@            Coverage Diff             @@
##           master     #902      +/-   ##
==========================================
- Coverage   97.30%   94.59%   -2.71%     
==========================================
  Files          97       97              
  Lines        6337     6348      +11     
  Branches      712      711       -1     
==========================================
- Hits         6166     6005     -161     
- Misses        102      272     +170     
- Partials       69       71       +2     

Impacted Files	Coverage Δ
imblearn/metrics/tests/test_classification.py	100.00% <ø> (ø)
imblearn/ensemble/_forest.py	89.93% <76.66%> (-2.38%)	⬇️
imblearn/_min_dependencies.py	100.00% <100.00%> (ø)
imblearn/datasets/tests/test_imbalance.py	100.00% <100.00%> (ø)
imblearn/ensemble/tests/test_weight_boosting.py	100.00% <100.00%> (ø)
imblearn/over_sampling/_smote/cluster.py	100.00% <100.00%> (ø)
imblearn/pipeline.py	94.56% <100.00%> (ø)
imblearn/tests/test_pipeline.py	99.12% <100.00%> (+<0.01%)	⬆️
imblearn/utils/estimator_checks.py	95.33% <100.00%> (+0.03%)	⬆️
imblearn/keras/tests/test_generator.py	7.93% <0.00%> (-92.07%)	⬇️
... and 5 more

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[freddyaboulton]

@glemaitre Do you know if there will be a release to get this out?

[glemaitre]

The release is already out (0.9.1):

• On PyPI: https://pypi.org/project/imbalanced-learn/
• On conda-forge channel: https://anaconda.org/conda-forge/imbalanced-learn

[freddyaboulton]

Thank you!