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Found 8 papers, 8 papers with code
Change Detection for Local Explainability in Evolving Data Streams
In particular, we show that local attributions can become obsolete each time the predictive model is updated or concept drift alters the data generating distribution.
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.
Dynamic Model Tree for Interpretable Data Stream Learning
Dynamic Model Trees are thus a powerful online learning framework that contributes to more lightweight and interpretable machine learning in data streams.
Deep Neural Networks and Tabular Data: A Survey
Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data.
On Baselines for Local Feature Attributions
Our experimental study illustrates the sensitivity of popular attribution models to the baseline, thus laying the foundation for a more in-depth discussion on sensible baseline methods for tabular data.
Learning Parameter Distributions to Detect Concept Drift in Data Streams
By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters.
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accurate predictions.
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
On one hand, we suggest to complement the catalogue of counterfactual quality measures [1] using a criterion to quantify the degree of difficulty for a certain counterfactual suggestion.
