hi-paris/XPER
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
This tool helps data analysts and economists understand which input factors most influence the predictive accuracy of their models. You provide your model and data, and it tells you which features are most important for your model's overall performance, rather than just individual predictions. This is for professionals who build and evaluate predictive models in fields like finance, marketing, or risk management.
No commits in the last 6 months. Available on PyPI.
Use this if you need to explain why your model performs well (or poorly) by identifying the key drivers of its overall accuracy (e.g., AUC or R²).
Not ideal if you primarily need to explain individual predictions or want to understand the contribution of features to a single output value.
Stars
18
Forks
1
Language
Python
License
—
Last pushed
Nov 28, 2024
Commits (30d)
0
Dependencies
11
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