https://github.com/solegalli/machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data
https://github.com/solegalli/machine-learning-imbalanced-data

data-science imbalanced-classification imbalanced-data imbalanced-learning machine-learning python

Last synced: 2 days ago
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Code repository for the online course Machine Learning with Imbalanced Data

• Host: GitHub
• URL: https://github.com/solegalli/machine-learning-imbalanced-data
• Owner: solegalli
• License: other
• Created: 2020-10-13T09:03:28.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2024-11-29T19:52:41.000Z (about 1 month ago)
• Last Synced: 2024-12-22T23:04:02.416Z (9 days ago)
• Topics: data-science, imbalanced-classification, imbalanced-data, imbalanced-learning, machine-learning, python
• Language: Jupyter Notebook
• Homepage: https://www.courses.trainindata.com/p/machine-learning-with-imbalanced-data
• Size: 22 MB
• Stars: 164
• Watchers: 6
• Forks: 214
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
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## Machine Learning with Imbalanced Data - Code Repository

[](https://www.trainindata.com/p/machine-learning-with-imbalanced-data)

**Launched**: November, 2020

**Updated**: August, 2024

Actively maintained.

[](https://www.trainindata.com/p/machine-learning-with-imbalanced-data)

## Links

- [Online Course](https://www.trainindata.com/p/machine-learning-with-imbalanced-data)

## 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. Types of cost

	2. Obtaining the Cost

	3. Missclassification Cost

	4. Bayes Risk

	5. MetaCost

7. **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

	4. Calibrating a Classifier

## Links

- [Online Course](https://www.trainindata.com/p/machine-learning-with-imbalanced-data)