thresholdboost 0.1.1

A custom ensemble machine learning package for binary classification.

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:

   git clone [https://github.com/YourUsername/ThresholdBoost.git](https://github.com/YourUsername/ThresholdBoost.git)
   cd ThresholdBoost