https://github.com/celestialtaha/unbalanced-dataset-classification

Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)
https://github.com/celestialtaha/unbalanced-dataset-classification

adaboost-classifier classification machine-learning rusboost smote-algorithm unbalanced-data

Last synced: about 1 month ago
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Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)

• Host: GitHub
• URL: https://github.com/celestialtaha/unbalanced-dataset-classification
• Owner: celestialtaha
• License: mit
• Created: 2021-04-09T07:00:50.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-07-23T16:53:59.000Z (almost 5 years ago)
• Last Synced: 2025-07-21T19:26:57.976Z (11 months ago)
• Topics: adaboost-classifier, classification, machine-learning, rusboost, smote-algorithm, unbalanced-data
• Language: Jupyter Notebook
• Homepage:
• Size: 720 KB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Unbalanced-dataset-Classification

Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)

Below is the detailed results:

![image](https://github.com/tahasamavati/Unbalanced-dataset-Classification/blob/main/results.png)

Average Classifier Precision for AdaBoost :          0.77

Average Classifier Precision for RUSBoost :          0.82

Average Classifier Precision for SMOTEBoost :        0.66

Average Classifier Precision for RandomBalanceBoost :0.6

Average Classifier Precision for RandomForest :      0.95

Average Classifier Precision for SVM :               1.0

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* Best performing method based on Average Precision of classifiers: "SVM"

* Best Performing Ensemble Classifier is "Random Forset" Runner up (second best) is RUSBOOST

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Taha Samavati - Analysis of final results