This project addresses one of the most common and challenging problems in machine learning: classification with highly imbalanced datasets. When classes are unevenly distributed, standard machine learning algorithms tend to favor the majority class, resulting in poor performance for minority classes.
Boosting Techniques Implemented
This repository focuses on boosting techniques specifically designed for imbalanced datasets:
- AdaBoost M2: An extension of AdaBoost for multiclass classification problems
- SMOTE Boost: Combines Synthetic Minority Over-sampling Technique with boosting
- RusBoost: Integrates random undersampling with boosting algorithms
Key Features
- Implementation of multiple boosting techniques for imbalanced data
- Comparative analysis of different approaches
- Performance evaluation using appropriate metrics for imbalanced classification
- Practical examples with real-world datasets
Technologies
- Python
- Scikit-learn
- Imbalanced-learn
- Jupyter Notebook
- Machine Learning
Applications
The techniques demonstrated in this project are applicable to numerous real-world scenarios:
- Fraud detection
- Medical diagnosis of rare conditions
- Anomaly detection
- Predictive maintenance
- Customer churn prediction
This project provides practical solutions for practitioners dealing with class imbalance problems in their machine learning workflows.