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08/30/2023
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning mode...
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06/19/2023
Interpreting Deep Neural Networks with the Package innsight
The R package innsight offers a general toolbox for revealing variable-w...
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10/06/2022
Conditional Feature Importance for Mixed Data
Despite the popularity of feature importance measures in interpretable m...
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08/12/2022
Unifying local and global model explanations by functional decomposition of low dimensional structures
We consider a global explanation of a regression or classification funct...
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05/19/2022
Smooth densities and generative modeling with unsupervised random forests
Density estimation is a fundamental problem in statistics, and any attem...
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09/03/2021
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Scientists and practitioners increasingly rely on machine learning to mo...
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01/28/2019
Testing Conditional Predictive Independence in Supervised Learning Algorithms
We propose a general test of conditional independence. The conditional p...
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01/18/2019
A Random Forest Approach for Modeling Bounded Outcomes
Random forests have become an established tool for classification and re...
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05/11/2016
Random forests for survival analysis using maximally selected rank statistics
The most popular approach for analyzing survival data is the Cox regress...
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08/18/2015
