M-Bagging is a groundbreaking ensemble learning technique that supercharges classification models. By fusing modified bootstrapping with heterogeneous classifiers, it achieves superior prediction accuracy.
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- Features
- Installation
- Demo
- Usage
- Datasets & Results
- My research Paper
- Contributing
- License & References
- Modified Bootstrapping: Special handling of misclassified samples and utilization of out-of-bag samples.
- Heterogeneous Classifiers: A blend of SVM, Naïve Bayes, KNN, Decision Trees, and Logistic Regression.
- Improved Prediction Accuracy: Outshines standard Bagging and rivals state-of-the-art models.
- Adaptability: Tailor it to various datasets and classification tasks.
Get started with just a few commands:
git clone https://github.com/your-username/m-bagging.git
cd m-bagging
pip install -r requirements.txt
Launch M-Bagging on your dataset with a single command:
python m_bagging.py --dataset path/to/dataset.csv
Tested on diverse datasets like Diabetes, Liver Disorder, and more, M-Bagging exhibits remarkable results.
Detailed Results | Datasets Info
Title: M-Bagging: A New Modified Bagging Classification Model to Improve Prediction Accuracy
In this research, I present M-Bagging, a novel model that significantly enhances traditional Bagging. The key contributions include special handling of misclassified samples, utilization of out-of-bag samples, various classifiers, and superior prediction accuracy.
For a comprehensive understanding of the methodology and results, please explore the full research paper.
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