Research Objective:
This research endeavors to demonstrate that ensemble learning methodologies, specifically bagging and boosting, yield superior performance compared to traditional machine learning models. By aggregating the outputs of multiple weak learners, the aim is to:
- Enhance model accuracy and generalization capabilities.
- Mitigate variance and overfitting phenomena.
- Provide empirical validation of the efficacy of ensemble methods over individual classifiers.
This study seeks to establish empirical evidence supporting ensemble learning as a more robust and dependable approach for machine learning tasks.
Current Results are in notebooks/results.ipynb