Cover Image

xplainfi: Feature Importance and Statistical Inference for Machine Learning in R

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Burk, Lukas, Ewald, Fiona Katharina, Casalicchio, Giuseppe, Wright, Marvin N., Bischl, Bernd
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Guide to Feature Importance Methods for Scientific Inference
by: Ewald, Fiona Katharina, et al.
Published: (2024)

Decomposing Global Feature Effects Based on Feature Interactions
by: Herbinger, Julia, et al.
Published: (2023)

CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
by: Ewald, Fiona Katharina, et al.
Published: (2026)

On the Robustness of Global Feature Effect Explanations
by: Baniecki, Hubert, et al.
Published: (2024)

Analyzing Error Sources in Global Feature Effect Estimation
by: Heiß, Timo, et al.
Published: (2026)

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
by: Burk, Lukas, et al.
Published: (2024)

Efficient and Accurate Explanation Estimation with Distribution Compression
by: Baniecki, Hubert, et al.
Published: (2024)

mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
by: Fischer, Sebastian, et al.
Published: (2026)

fmeffects: An R Package for Forward Marginal Effects
by: Löwe, Holger, et al.
Published: (2023)

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
by: Scholbeck, Christian A., et al.
Published: (2023)

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025)

mlr3summary: Concise and interpretable summaries for machine learning models
by: Dandl, Susanne, et al.
Published: (2024)

Optimal Transport Group Counterfactual Explanations
by: Valero-Leal, Enrique, et al.
Published: (2026)

Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)

Position: Why We Must Rethink Empirical Research in Machine Learning
by: Herrmann, Moritz, et al.
Published: (2024)

Privilege Scores
by: Bothmann, Ludwig, et al.
Published: (2025)

Imputation Uncertainty in Interpretable Machine Learning Methods
by: Golchian, Pegah, et al.
Published: (2025)

CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
by: Dandl, Susanne, et al.
Published: (2024)

Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
by: Koenen, Niklas, et al.
Published: (2024)

Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
by: Wimmer, Lisa, et al.
Published: (2025)

Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
by: Weber, Tobias, et al.
Published: (2023)

Revisiting Unbiased Implicit Variational Inference
by: Pielok, Tobias, et al.
Published: (2025)

Statistical Significance of Feature Importance Rankings
by: Goldwasser, Jeremy, et al.
Published: (2024)

mlr3mbo: Bayesian Optimization in R
by: Becker, Marc, et al.
Published: (2026)

Calibrating LLMs with Information-Theoretic Evidential Deep Learning
by: Li, Yawei, et al.
Published: (2025)

Structured Credal Learning
by: Venkatesh, Varun, et al.
Published: (2026)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
by: Kolb, Chris, et al.
Published: (2025)

Overtuning in Hyperparameter Optimization
by: Schneider, Lennart, et al.
Published: (2025)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
by: Pielok, Tobias, et al.
Published: (2025)

Feature Importance and Explainability in Quantum Machine Learning
by: Power, Luke, et al.
Published: (2024)

Effector: A Python package for regional explanations
by: Gkolemis, Vasilis, et al.
Published: (2024)

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
by: Feurer, Matthias, et al.
Published: (2022)

Deep Learning for Survival Analysis: A Review
by: Wiegrebe, Simon, et al.
Published: (2023)

Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
by: Dorigatti, Emilio, et al.
Published: (2022)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
by: Sommer, Emanuel, et al.
Published: (2024)

A Dual-Perspective Approach to Evaluating Feature Attribution Methods
by: Li, Yawei, et al.
Published: (2023)

Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)

Interpreting Deep Neural Networks with the Package innsight
by: Koenen, Niklas, et al.
Published: (2023)

Differentiable Sparsity via $D$-Gating: Simple and Versatile Structured Penalization
by: Kolb, Chris, et al.
Published: (2025)

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
by: Nagler, Thomas, et al.
Published: (2024)