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Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
We present a brief history of the field of interpretable machine learnin...
10/19/2020 ∙ by Christoph Molnar, et al. ∙
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Pitfalls to Avoid when Interpreting Machine Learning Models
Modern requirements for machine learning (ML) models include both high p...
07/08/2020 ∙ by Christoph Molnar, et al. ∙
66
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Model-agnostic Feature Importance and Effects with Dependent Features – A Conditional Subgroup Approach
Partial dependence plots and permutation feature importance are popular ...
06/08/2020 ∙ by Christoph Molnar, et al. ∙
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Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations
Non-linear machine learning models often trade off a great predictive pe...
04/08/2019 ∙ by Christian A. Scholbeck, et al. ∙
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Component-Wise Boosting of Targets for Multi-Output Prediction
Multi-output prediction deals with the prediction of several targets of ...
04/08/2019 ∙ by Quay Au, et al. ∙
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Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition
To obtain interpretable machine learning models, either interpretable mo...
04/08/2019 ∙ by Christoph Molnar, et al. ∙
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Visualizing the Feature Importance for Black Box Models
In recent years, a large amount of model-agnostic methods to improve the...
04/18/2018 ∙ by Giuseppe Casalicchio, et al. ∙
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OpenML Benchmarking Suites and the OpenML100
We advocate the use of curated, comprehensive benchmark suites of machin...
08/11/2017 ∙ by Bernd Bischl, et al. ∙
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Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machi...
03/27/2017 ∙ by Philipp Probst, et al. ∙
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OpenML: An R Package to Connect to the Machine Learning Platform OpenML
OpenML is an online machine learning platform where researchers can easi...
01/05/2017 ∙ by Giuseppe Casalicchio, et al. ∙
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