linkedin/FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
This project helps data scientists, machine learning engineers, and researchers quickly understand why their tree-based models (like Random Forest or XGBoost) make specific predictions. It takes your trained tree model and a dataset, then efficiently calculates 'SHAP values' that show how much each input feature contributed to each prediction. This allows you to explain complex model behaviors in an understandable way, especially for large datasets.
554 stars. Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you need to interpret predictions from large tree-based machine learning models faster and more efficiently, especially with big datasets or when using parallel computing.
Not ideal if you are working with non-tree-based models (e.g., neural networks) or if your datasets are very small where the original TreeSHAP performance is already sufficient.
Stars
554
Forks
38
Language
Python
License
BSD-2-Clause
Category
Last pushed
Jun 26, 2023
Commits (30d)
0
Dependencies
11
Reverse dependents
1
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