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06/11/2022
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
Interpretable machine learning (IML) is concerned with the behavior and ...
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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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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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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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07/16/2020
Relative Feature Importance
Interpretable Machine Learning (IML) methods are used to gain insight in...
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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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04/23/2020
Multi-Objective Counterfactual Explanations
Counterfactual explanations are one of the most popular methods to make ...
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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
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