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Found 11 papers, 7 papers with code
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent.
Marginal Effects for Non-Linear Prediction Functions
Hence, marginal effects are typically used as approximations for feature effects, either in the shape of derivatives of the prediction function or forward differences in prediction due to a change in a feature value.
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions.
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
To address the challenges and advance the field, we urge to recall our roots of interpretable, data-driven modeling in statistics and (rule-based) ML, but also to consider other areas such as sensitivity analysis, causal inference, and the social sciences.
Relative Feature Importance
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
In addition, we apply the conditional subgroups approach to partial dependence plots (PDP), a popular method for describing feature effects that can also suffer from extrapolation when features are dependent and interactions are present in the model.
Multi-Objective Counterfactual Explanations
We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations.
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models.
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model.
Visualizing the Feature Importance for Black Box Models
Based on local feature importance, we propose two visual tools: partial importance (PI) and individual conditional importance (ICI) plots which visualize how changes in a feature affect the model performance on average, as well as for individual observations.
