Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ksharma67/partial-dependent-plots-individual-conditional-expectation-plots-with-shap
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.
https://github.com/ksharma67/partial-dependent-plots-individual-conditional-expectation-plots-with-shap
eda individual-conditional-expectation matplotlib numpy pandas partial-dependence-plot python seaborn shap shapley-additive-explanations sklearn xgboost
Last synced: 11 days ago
JSON representation
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.
- Host: GitHub
- URL: https://github.com/ksharma67/partial-dependent-plots-individual-conditional-expectation-plots-with-shap
- Owner: ksharma67
- Created: 2022-12-03T17:59:57.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-03T18:07:05.000Z (almost 2 years ago)
- Last Synced: 2023-03-08T12:19:51.435Z (over 1 year ago)
- Topics: eda, individual-conditional-expectation, matplotlib, numpy, pandas, partial-dependence-plot, python, seaborn, shap, shapley-additive-explanations, sklearn, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0