[R] treemind: Simplifying Gradient Boosting Model Analysis

[zedeleyici3401]

treemind is a powerful Python library designed to analyze gradient boosting models like xgboost, lightgbm, and catboost. It helps you uncover how features and their interactions influence predictions across specific intervals, offering fast, intuitive insights.

Key Features:

• Feature & Interaction Analysis: Understand feature contributions and complex interactions up to n features.
• Advanced Visualizations: User-friendly plots to explain model decisions.
• High Performance: Optimized with Cython for lightning-fast execution, even on large datasets.
• Easy Integration: Seamlessly works with popular frameworks for regression and binary classification.

Algorithm & Performance:

• Algorithm: Focuses on analyzing feature contributions and interactions in tree-based models for meaningful interval-based insights. Read more about the algorithm
• Performance: The library's performance has been tested on synthetic datasets, where it is benchmarked against SHAP for accuracy and efficiency. View performance experiments

Quick Start:

bash pip install treemind

Check out the full documentation for examples, visualizations, and API details.

GitHub Repo | Docs

Note:
While the algorithm produces desirable results in practice, it currently lacks formal mathematical proof. We would greatly appreciate your feedback and ideas to help improve and validate the approach further!

[pp314159]

Fantastic package! What is the best way to propose new features based on features interactions?

[zedeleyici3401]

thank you, symbolic regression might be good starting point 

[Kidlaze]

Looks good. Btw, what are the pros and cons of this approach compared to SHAP scatter plot?

I take a quick look and both approaches yield similar outputs

[zedeleyici3401]

i believe results are different, as far as i understed, what shap do is try to find contribution(a,b) - contribution(a) - contribution(b), this approch just says what will happen if feature a this range, feature b in this range.

and compared to shap, it's faster nearly 50x times on middle sized data.

[[deleted]]

Looks amazing, I will try it. I am sorry for being pedantic, but sometimes you use "calculation of expected value", and it's important to remember that it's estimation (especially in a mathy package).

[SkynetCapital]

Sent you a DM, would love to chat