AdaBoost Implementation for DATA 2060 Final Project from AI alchemist

This repository contains the AI alchemist's implementation for Adaboost.

Project Overview

AdaBoost, or Adaptive Boosting, is a machine learning ensemble technique that combines multiple "weak" classifiers to form a "strong" classifier. This project focuses on:

-Deriving and explaining the core mathematical principles behind AdaBoost.

-Implementing the algorithm from scratch in Python.

-Demonstrating its application on an example dataset with clear visualizations.

Environment Setup

To reproduce the results, ensure you have the following software and library versions installed:

• Python: 3.12.5
• NumPy: 2.0.1
• Pandas: 2.2.2
• Matplotlib: 3.9.1
• Scikit-learn: 1.5.1

Installation

1. Clone the repository:

   git clone https://github.com/Sizchode/Data2060-Final-Project.git
   cd Data2060-Final-Project
   

2. Create a virtual environment using the provided YAML file:

   pip install cryptography
   conda env create -f environment.yaml
   

3. Activate the environment:

   conda activate adaboost_project
   

Authors

This project was developed by:

Junhan Liu: junhan_liu@brown.edu

Zhenke Liu: zhenke_liu@brown.edu

Qingyu Wang: qingyu_wang@brown.edu

Justin Xiao: xulong_xiao@brown.edu