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Found 6 papers, 2 papers with code
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics.
fmeffects: An R Package for Forward Marginal Effects
Forward marginal effects (FMEs) have recently been introduced as a versatile and effective model-agnostic interpretation method.
Algorithm-Agnostic Interpretations for Clustering
The partial dependence for clustering evaluates average changes in cluster assignments for the entire feature space.
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.
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.
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.
