How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
A python library for decision tree visualization and model interpretation.
The main features of parrt/dtreeviz are: Decision Tree Models, Model Visualization, Visualization.
Projects with overlapping indexed features include: federatedai/fate — FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train… astrofrog/fast-histogram — :zap: Fast 1D and 2D histogram functions in Python :zap:. bokeh/bokeh — Bokeh is a Python data visualization library and interactive plotting framework used to create high-performance… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… cerlymarco/linear-tree — A python library to build Model Trees with Linear Models at the leaves. astrofrog/mpl-scatter-density — :zap: Fast scatter density plots for Matplotlib :zap:.
FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train machine learning models without exposing raw data to any party. It provides a complete framework for private data collaboration, allowing participants to jointly compute on sensitive information while maintaining data privacy and security guarantees through secure multi-party computation protocols. The platform distinguishes itself through its comprehensive infrastructure management capabilities, supporting automated deployment of multi-party clusters using Ansible-driven provisioni
:zap: Fast 1D and 2D histogram functions in Python :zap:
Bokeh is a Python data visualization library and interactive plotting framework used to create high-performance graphics and data dashboards that render in web browsers. It serves as a tool for generating standalone HTML documents, embedded components for digital notebooks, and full-stack web applications powered by a Python backend. The project distinguishes itself through its ability to handle large or streaming datasets while maintaining smooth interactivity. It enables linked brushing across multiple views, allowing data selected in one plot to automatically highlight corresponding data i
:zap: Fast scatter density plots for Matplotlib :zap: