Code repository for the online course Machine Learning with Imbalanced Data

solegalli Last update: Dec 16, 2023

Machine Learning with Imbalanced Data - Code Repository

Published November, 2020

Actively maintained.

Links

Online Course

Table of Contents

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
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
Oversampling methods
1. Random Oversampling
2. ADASYN
3. SMOTE
4. BorderlineSMOTE
5. KMeansSMOTE
6. SMOTENC
7. SVMSMOTE
Over and Undersampling Methods
1. SMOTENN
2. SMOTETomek
Ensemble Methods
1. Coming Soon
Cost Sensitive Learning
1. Types of cost
2. Obtaining the Cost
3. Missclassification Cost
4. Bayes Risk
5. MetaCost
Probability Calibration
1. Probability Calibration Curves
2. Brier Score
3. Effect of under and over sampling on Probability Calibration
4. Cost Sensitive Learning and Probability Calibration
5. Calibrating a Classifier

Tags:

imbalanced-classification

imbalanced-data

imbalanced-learning

machine-learning

Owner

solegalli

GitHub Repository https://github.com/solegalli

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