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Limitations of the Accuracy\n\t2. Precision, Recall, F-Measure\n\t3. Confusion Matrix\n\t4. False Positive Rate and False Negative Rate\n\t5. Geometric Mean\n\t6. Dominance\n\t7. Index of imbalanced accuracy\n\t8. ROC-AUC\n\t9. Precision-Recall Curves\n\t10. Probability Distribution and Calibration\n\t11. Which metric to optimise\n\n\n2. **Udersampling Methods**\n\t1. Random Undersampling\n\t2. Condensed Nearest Neighbour\n    3. Tomek Links\n    4. One Sided Selection\n\t5. Edited Nearest Neighbours\n\t6. Repeated Edited Nearest Neighbours\n\t7. All KNN\n\t8. Neighbourhood Cleaning Rule    \n\t9. NearMiss\n\t10. Instance Hardness Threshold\n\n\n3. **Oversampling methods**\n\t1. Random Oversampling\n\t2. ADASYN\n\t3. SMOTE\n\t4. BorderlineSMOTE\n\t5. KMeansSMOTE\n\t6. SMOTENC\n\t7. SVMSMOTE\n\n\n4. **Over and Undersampling Methods**\n\t1. SMOTENN\n\t2. SMOTETomek\n\n\n5. **Ensemble Methods**\n\t1. Coming Soon\n\n\n6. **Cost Sensitive Learning**\n\t1. Types of cost\n\t2. Obtaining the Cost\n\t3. Missclassification Cost\n\t4. Bayes Risk\n\t5. MetaCost\n\n7. **Probability Calibration**\n\t1. Probability Calibration Curves\n\t2. Brier Score\n\t3. Effect of under and over sampling on Probability Calibration\n\t4. Cost Sensitive Learning and Probability Calibration\n\t4. Calibrating a Classifier\n\n## Links\n\n- [Online Course](https://www.trainindata.com/p/machine-learning-with-imbalanced-data)","funding_links":["https://github.com/sponsors/solegalli"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsolegalli%2Fmachine-learning-imbalanced-data","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsolegalli%2Fmachine-learning-imbalanced-data","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsolegalli%2Fmachine-learning-imbalanced-data/lists"}