Metadata-Version: 2.4
Name: thresholdboost
Version: 0.1.1
Summary: A custom ensemble machine learning package for binary classification.
Home-page: https://github.com/IJPverse/ThresholdBoost
Author: Israt Jahan Powsi, Rayhan Miah, Md Khorshed Alam
Author-email: ijahan23.phy@bu.ac.bd, rmiah19.phy@bu.ac.bd, dmkalam@bu.ac.bd
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: scikit-learn>=1.0.2
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
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Dynamic: summary

# ThresholdBoost 

**ThresholdBoost** is a custom-built, ensemble-based machine learning algorithm designed from scratch. It provides a robust, iterative approach to binary classification tasks, making it highly effective for scientific data analysis, including materials science and medical datasets.

##  Key Features
* **Custom Architecture:** Built entirely from scratch without relying on pre-existing ensemble wrappers (e.g., AdaBoost or XGBoost).
* **High Accuracy:** Optimized for complex, multi-dimensional tabular datasets.
* **Scalable & Lightweight:** Efficiently handles large datasets with minimal computational overhead.
* **Universal Application:** Can be utilized for general-purpose classification tasks beyond its initial domain.

##  Scientific Application (Case Study)
This algorithm was rigorously tested on a **Material Science Dataset,** **Photoelectrochemical (PEC) Efficiency dataset,** **MDAnalysis Dataset,**, ** Astrophysics - exo planet classification,** **Quantum topological properties prediction** . 
* **Performance:** Achieved high accuracy and generalized well on unseen test data.

##  Installation & Requirements

To run the ThresholdBoost package locally, ensure you have Python 3.8+ installed. 

1. Clone the repository:
   ```bash
   git clone [https://github.com/YourUsername/ThresholdBoost.git](https://github.com/YourUsername/ThresholdBoost.git)
   cd ThresholdBoost
