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LMU Munich, Munich Center for Machine Learning (MCML)
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SAILS reveals the functional forms of feature interactions in machine learning models, transforming how we interpret model behavior beyond mere detection.
Adaptive coalition selection in ShaplEIG boosts Shapley value estimation efficiency, slashing computational complexity and enhancing performance in resource-constrained settings.
R users gain a powerful, extensible deep learning framework with `mlr3torch`, seamlessly integrating neural networks into the familiar `mlr3` ecosystem for streamlined experimentation and deployment.
Forget holdout data for feature effect estimation: training data's larger sample size usually wins, and cross-validation can further reduce model variance.
Unlock robust feature importance analysis with `xplainfi`, an R package that fills critical gaps by offering conditional importance methods and statistical inference for diverse ML models.
Questioning the common practice of interpreting data through a single model class, this work reveals the existence of alternative well-performing models across multiple model classes and their hyperparameters.
