Search Results - "Loecher, Markus"

Debiasing SHAP scores in random forests by Loecher, Markus

“…Black box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice…”
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The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits by Loecher, Markus

“…The connection between optimal stopping times of American Options and multi-armed bandits is the subject of active research. This article investigates the…”
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RgoogleMaps and loa : Unleashing R Graphics Power on Map Tiles by Loecher, Markus, Ropkins, Karl

“…The RgoogleMaps package provides (1) an R interface to query the Google and the OpenStreetMap servers for static maps in the form of PNGs, and (2) enables the…”
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Unbiased variable importance for random forests by Loecher, Markus

“…The default variable-importance measure in random forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting…”
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Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning by Pfeifer, Bastian, Gevaert, Arne, Loecher, Markus, Holzinger, Andreas

“…Random Forests (RFs) are powerful ensemble learning algorithms that are widely used in various machine learning tasks. However, they tend to overfit noisy or…”
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Real-world insights on public charging demand and infrastructure use from electric vehicles by Hardinghaus, Michael, Löcher, Markus, Anderson, John E

“…The city of Berlin has significantly expanded public charging infrastructure for electric vehicles. As a result of this investment, real-world charging data…”
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Enhanced predictability of hierarchical propagation in complex networks by Loecher, Markus, Kadtke, James

“…We apply recent results on the dynamics of random walkers to the analysis of idealized epidemic outbreaks in scale-free networks. By replacing the node degree…”
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From unbiased MDI Feature Importance to Explainable AI for Trees by Loecher, Markus

“…We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the the default…”
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Unbiased variable importance for random forests by Loecher, Markus

“…The default variable-importance measure in random Forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting…”
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Data-driven advice for interpreting local and global model predictions in bioinformatics problems by Loecher, Markus, Wu, Qi

“…Tree-based algorithms such as random forests and gradient boosted trees continue to be among the most popular and powerful machine learning models used across…”
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