Welcome to DimRed, the comprehensive Python toolkit for applying advanced dimensionality reduction techniques to enhance machine learning model efficiency and effectiveness. Our toolkit includes a variety of dimensionality reduction methods, tailored to simplify the complexities of high-dimensional data, making it easier to visualize, analyze, and gain insights from your data.
- Versatile Reduction Techniques: Implements well-known methods like PCA, SparsePCA, KernelPCA, and emerging techniques such as Isomap and Locally Linear Embedding.
- Enhanced Model Training: Integrates with popular machine learning libraries including sklearn, xgboost, and lightgbm to evaluate the performance impact of each dimensionality reduction method.
- Real-time Performance Monitoring: Utilizes Weights & Biases for real-time tracking and analysis of model performance.
- Comprehensive Evaluation: Our rigorous testing framework ensures that each method is evaluated for maximum performance improvement.
Clone this repository to your local machine to get started:
git clone https://github.com/Programmer-RD-AI/Dimensionality-Reduction.git
cd Dimensionality-ReductionInstall the required dependencies:
pip install -r requirements.txt
conda install --file conda_requirements.txtExplore detailed usage examples and get started with different dimensionality reduction techniques in the /Revealing Dimensional Reduction in Data Mining 9ee23d2c42c444fcb16bae6e5bedd770.pdf directory.
Contributions to DimRed are welcome! We encourage contributions in the form of bug fixes, new features, or documentation improvements.
DimRed is licensed under the MIT license. See the LICENSE file for more details.
This project utilizes insights and methodologies from across the field of data science to provide a robust toolkit for researchers and developers. Special thanks to the machine learning community for their ongoing contributions to open source.