620 citations · 1.3k across the 11 of their papers we have counts for
11 papers
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
Timo Freiesleben, Gunnar König, Christoph Molnar +1
To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful pre…
Marginal Effects for Non-Linear Prediction Functions
Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar +2
Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models and especially generalized linear models,…
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…
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
We present a brief history of the field of interpretable machine learning (IML), give an overview of state-of-the-art interpretation methods, and discuss challenges. Research in IM…
Relative Feature Importance
Gunnar König, Christoph Molnar, Bernd Bischl +1
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ i…
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Christoph Molnar, Gunnar König, Julia Herbinger +6
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and S…
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
Christoph Molnar, Gunnar König, Bernd Bischl +1
The interpretation of feature importance in machine learning models is challenging when features are dependent. Permutation feature importance (PFI) ignores such dependencies, whic…
Multi-Objective Counterfactual Explanations
Susanne Dandl, Christoph Molnar, Martin Binder +1
Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `wha…
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Christian A. Scholbeck, Christoph Molnar, Christian Heumann +2
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are r…
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can…
Visualizing the Feature Importance for Black Box Models
Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduc…