TreeModelVis 0.1.0

A toolkit for visualizing and customizing tree-based models.

TreeModelVis: Enhanced Tree-Based Model Visualization

TreeModelVis is a versatile Python toolkit for visualizing and customizing tree-based models, including decision trees and ensembles like Random Forests and Gradient Boosting. Engineered for seamless integration with scikit-learn, TreeModelVis delivers enhanced interpretability and detailed visualization capabilities, making it an indispensable tool for model analysis and presentation.

Features

• Versatile Visualization: Create detailed, customizable graphics to visualize decision trees and tree ensembles.
• Out-of-Sample Data Analysis: Evaluate out-of-sample data and compare its error alignment with the training error, offering insights into the model's generalization.
• Data Distribution Insights: Gain an understanding of data distribution and model decision patterns to improve interpretability.
• scikit-learn Integration: Work smoothly with existing scikit-learn workflows for a streamlined experience.
• User-Friendly: Accessible to users of all levels, from beginners to experienced practitioners.

Quickstart

To get started with TreeModelVis, install the package using pip:

pip install TreeModelVis

Or clone the repository and install the requirements:

git clone https://github.com/yourusername/TreeModelVis.git
cd TreeModelVis
pip install -r requirements.txt