Search Results for author:
Found 5 papers, 2 papers with code
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Existing methods for explainable artificial intelligence (XAI), including popular feature importance measures such as SAGE, are mostly restricted to the batch learning scenario.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Approximating the Shapley Value without Marginal Contributions
The Shapley value, which is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, has recently been used intensively in explainable artificial intelligence.
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams
Explainable Artificial Intelligence (XAI) has mainly focused on static learning scenarios so far.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
