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| SHAP (SHapley Additive exPlanations)× | Logistisk regression× | |
|---|---|---|
| Fagområde≠ | Maskinlæring | Forskningsstatistik |
| Familie≠ | Machine learning | Process / pipeline |
| Oprindelsesår≠ | 2017 | 1958 |
| Ophavsperson≠ | Lundberg, S.M. & Lee, S.-I. | David Roxbee Cox |
| Type≠ | Model-explanation method (Shapley-value attribution) | Method |
| Oprindelig kilde≠ | Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Aliasser≠ | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability | logit model, binomial logistic regression, LR |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did). | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
| ScholarGateDatasæt ↗ |
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