150 citations · 189 across the 6 of their papers we have counts for
6 papers
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2
We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance met…
Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model e…
Fast Estimation of Partial Dependence Functions using Trees
Jinyang Liu, Tessa Steensgaard, Marvin N. Wright +2
Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the…
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Lukas Burk, John Zobolas, Bernd Bischl +3
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in…
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…
Unbiased split variable selection for random survival forests using maximally selected rank statistics
Marvin N. Wright, Theresa Dankowski, Andreas Ziegler
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be…