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06/01/2023
Decomposing Global Feature Effects Based on Feature Interactions
Global feature effect methods, such as partial dependence plots, provide...
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05/04/2023
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
This work introduces interpretable regional descriptors, or IRDs, for lo...
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04/13/2023
counterfactuals: An R Package for Counterfactual Explanation Methods
Counterfactual explanation methods provide information on how feature va...
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09/21/2022
Algorithm-Agnostic Interpretations for Clustering
A clustering outcome for high-dimensional data is typically interpreted ...
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06/11/2022
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution
Despite all the benefits of automated hyperparameter optimization (HPO),...
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02/15/2022
REPID: Regional Effect Plots with implicit Interaction Detection
Machine learning models can automatically learn complex relationships, s...
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01/21/2022
Marginal Effects for Non-Linear Prediction Functions
Beta coefficients for linear regression models represent the ideal form ...
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11/08/2021
Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to...
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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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07/28/2021
Developing Open Source Educational Resources for Machine Learning and Data Science
Education should not be a privilege but a common good. It should be open...
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06/15/2021
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Global model-agnostic feature importance measures either quantify whethe...
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06/04/2021
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results
In recent years, the need for neutral benchmark studies that focus on th...
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04/23/2021
Grouped Feature Importance and Combined Features Effect Plot
Interpretable machine learning has become a very active area of research...
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10/19/2020
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
We present a brief history of the field of interpretable machine learnin...
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07/08/2020
Pitfalls to Avoid when Interpreting Machine Learning Models
Modern requirements for machine learning (ML) models include both high p...
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06/08/2020
Model-agnostic Feature Importance and Effects with Dependent Features – A Conditional Subgroup Approach
Partial dependence plots and permutation feature importance are popular ...
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04/08/2019
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations
Non-linear machine learning models often trade off a great predictive pe...
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04/08/2019
Component-Wise Boosting of Targets for Multi-Output Prediction
Multi-output prediction deals with the prediction of several targets of ...
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04/08/2019
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition
To obtain interpretable machine learning models, either interpretable mo...
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04/18/2018
Visualizing the Feature Importance for Black Box Models
In recent years, a large amount of model-agnostic methods to improve the...
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08/11/2017
OpenML Benchmarking Suites and the OpenML100
We advocate the use of curated, comprehensive benchmark suites of machin...
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03/27/2017
Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machi...
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01/05/2017
