3.5k citations · 5.3k across the 11 of their papers we have counts for
12 papers
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein +3
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread a…
Interpreting Deep Neural Networks with the Package innsight
Niklas Koenen, Marvin N. Wright
The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside f…
Conditional Feature Importance for Mixed Data
Kristin Blesch, David S. Watson, Marvin N. Wright
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical pers…
Unifying local and global model explanations by functional decomposition of low dimensional structures
Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a ne…
Adversarial random forests for density estimation and generative modeling
David S. Watson, Kristin Blesch, Jan Kapar +1
We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests. Inspired by generative adversarial networks, we implement a recursiv…
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Christoph Molnar, Timo Freiesleben, Gunnar König +3
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer exp…
Generalization of the Change of Variables Formula with Applications to Residual Flows
Niklas Koenen, Marvin N. Wright, Peter Maaß +1
Normalizing flows leverage the Change of Variables Formula (CVF) to define flexible density models. Yet, the requirement of smooth transformations (diffeomorphisms) in the CVF pose…
A Random Forest Approach for Modeling Bounded Outcomes
Leonie Weinhold, Matthias Schmid, Marvin N. Wright +1
Random forests have become an established tool for classification and regression, in particular in high-dimensional settings and in the presence of complex predictor-response relat…
Testing Conditional Independence in Supervised Learning Algorithms
David S. Watson, Marvin N. Wright
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a red…
Hyperparameters and Tuning Strategies for Random Forest
Philipp Probst, Marvin Wright, Anne-Laure Boulesteix
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are dra…
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Marvin N. Wright, Andreas Ziegler
We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression an…
On the use of Harrell's C for clinical risk prediction via random survival forests
Matthias Schmid, Marvin Wright, Andreas Ziegler
Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble…