VisRuler: Visual analytics for extracting decision rules from bagged and boosted decision trees
Bagging and boosting are two popular ensemble methods in machine learning (ML) that
produce many individual decision trees. Due to the inherent ensemble characteristic of …
produce many individual decision trees. Due to the inherent ensemble characteristic of …
[HTML][HTML] Visual exploration of multi-dimensional data via rule-based sample embedding
We propose an approach to learning sample embedding for analyzing multi-dimensional
datasets. The basic idea is to extract rules from the given dataset and learn the embedding …
datasets. The basic idea is to extract rules from the given dataset and learn the embedding …
Deforestvis: Behaviour analysis of machine learning models with surrogate decision stumps
As the complexity of machine learning (ML) models increases and their application in
different (and critical) domains grows, there is a strong demand for more interpretable and …
different (and critical) domains grows, there is a strong demand for more interpretable and …
RuleExplorer: A scalable matrix visualization for understanding tree ensemble classifiers
The high performance of tree ensemble classifiers benefits from a large set of rules, which,
in turn, makes the models hard to understand. To improve interpretability, existing methods …
in turn, makes the models hard to understand. To improve interpretability, existing methods …
[PDF][PDF] The Life Cycle of Data Labels in Organizational Learning: a Case Study of the Automotive Industry.
Data labels are an integral input to develop machine learning (ML) models. In complex
domains, labels represent the externalized product of complex knowledge. While prior …
domains, labels represent the externalized product of complex knowledge. While prior …