https://github.com/sayande01/ensemble_learning_2
This project explores Bagging (Bootstrap Aggregating) for both classification and regression tasks. By training multiple models on different data subsets, Bagging improves accuracy and resilience. Using synthetic and real-world datasets, the project demonstrates how Bagging enhances performance over single-model approaches.
https://github.com/sayande01/ensemble_learning_2
bagging-ensemble random-forest-classifier
Last synced: 4 months ago
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This project explores Bagging (Bootstrap Aggregating) for both classification and regression tasks. By training multiple models on different data subsets, Bagging improves accuracy and resilience. Using synthetic and real-world datasets, the project demonstrates how Bagging enhances performance over single-model approaches.
- Host: GitHub
- URL: https://github.com/sayande01/ensemble_learning_2
- Owner: sayande01
- Created: 2024-08-11T19:15:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-11T19:16:53.000Z (almost 2 years ago)
- Last Synced: 2025-04-06T20:34:47.664Z (about 1 year ago)
- Topics: bagging-ensemble, random-forest-classifier
- Homepage:
- Size: 260 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
**Title:**
Enhancing Model Performance with Bagging: Classification and Regression Analysis
**Description:**
This project explores the application of Bagging (Bootstrap Aggregating) in both classification and regression tasks. Bagging is a robust ensemble learning technique that aims to improve model accuracy by training multiple instances of a base model on different subsets of the training data. This project uses synthetic and real-world datasets to demonstrate how Bagging can enhance the performance of classifiers and regressors compared to standard single-model approaches. Specifically, the project applies Bagging to a classification problem using the `make_moons` dataset and a regression problem using a placement dataset.
**Objective:**
1. **Understand Bagging:** Learn the fundamentals of Bagging, including its mechanism and advantages such as reducing overfitting, improving prediction accuracy, and handling noisy or imbalanced data.
2. **Apply Bagging in Classification:** Implement Bagging with Support Vector Classifiers (SVC) and compare its performance against Random Forest Classifiers on a synthetic dataset.
3. **Apply Bagging in Regression:** Implement Bagging with Linear Regression and evaluate its performance against Random Forest Regressors on a real-world placement dataset.
4. **Evaluate Model Performance:** Compare the effectiveness of Bagging with other ensemble methods and assess improvements in prediction accuracy and model stability for both classification and regression tasks.