{"library":"imblearn","type":"library","category":null,"description":"imbalanced-learn is a Python library that provides a comprehensive suite of resampling techniques to address imbalanced datasets in machine learning, where one class significantly outnumbers another. It offers methods for over-sampling (e.g., SMOTE, ADASYN), under-sampling (e.g., NearMiss, EditedNearestNeighbours), and combined approaches, along with ensemble methods tailored for imbalanced data. It is compatible with scikit-learn and is part of scikit-learn-contrib projects. The current stable version is 0.14.1, with regular maintenance releases to ensure compatibility with scikit-learn and Python versions.","language":"python","status":"active","version":"0.14.1","tags":["machine-learning","imbalanced-data","resampling","oversampling","undersampling","smote","scikit-learn","data-preprocessing"],"last_verified":"Fri May 22","install":[{"cmd":"pip install imbalanced-learn","imports":["import imblearn","from imblearn.over_sampling import SMOTE","from imblearn.over_sampling import RandomOverSampler","from imblearn.under_sampling import RandomUnderSampler","from imblearn.pipeline import Pipeline"]},{"cmd":"conda install -c conda-forge imbalanced-learn","imports":[]}],"homepage":"https://pypi.python.org/pypi/imbalanced-learn/","github":null,"docs":null,"changelog":null,"pypi":"https://pypi.org/project/imblearn/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":9.5,"avg_import_s":4.16,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/imblearn/compatibility"}}