maybeee18 / machine-learning-imbalanced-data

Machine Learning with Imbalanced Data- Code Repository

Published November, 2020

Links

• Online Course

Table of Contents

1. Metrics

   1. Limitations of the Accuracy
   2. Precision, Recall, F-Measure
   3. Confusion Matrix
   4. False Positive Rate and False Negative Rate
   5. Geometric Mean
   6. Dominance
   7. Index of imbalanced accuracy
   8. ROC-AUC
   9. Precision-Recall Curves
   10. Probability Distribution and Calibration
   11. Which metric to optimise

2. Udersampling Methods

   1. Random Undersampling
   2. Condensed Nearest Neighbour
   3. Tomek Links
   4. One Sided Selection
   5. Edited Nearest Neighbours
   6. Repeated Edited Nearest Neighbours
   7. All KNN
   8. Neighbourhood Cleaning Rule
   9. NearMiss
   10. Instance Hardness Threshold

3. Oversampling methods

   1. Random Oversampling
   2. ADASYN
   3. SMOTE
   4. BorderlineSMOTE
   5. KMeansSMOTE
   6. SMOTENC
   7. SVMSMOTE

4. Over and Undersampling Methods

   1. SMOTENN
   2. SMOTETomek

5. Ensemble Methods

   1. Coming Soon

6. Cost Sensitive Learning

   1. Coming Soon