A unified approach to explain the output of any machine learning model.
Project description
SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distributions
Close
Hashes for shap_legacy-0.36.0-cp37-cp37m-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1d088e3ae3991be48d56af4c66d45bbcc8b65df9454338b82659aea9e0290322 |
|
| MD5 | b54f20c80858de632adfda2226d1b76f |
|
| BLAKE2b-256 | b6fda300a88506ac26354796b88e71ad2e5a3891ef6c09c7c787c75b71da218d |
Close
Hashes for shap_legacy-0.36.0-cp36-cp36m-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 5d3d1efd72ed1c1e44864f57a323f4d2ec9077530ce032ba4ee572404ca1ba29 |
|
| MD5 | 56246ba44c1088e08b423a106ae6fa0a |
|
| BLAKE2b-256 | 99b7cee0db31121080f90eacc8dd8130c4aca7c7bd7375d9cc3a619c1e43effa |
