MAGIX: Model Agnostic Globally Interpretable Explanations
By Nikaash Puri, Piyush Kumar Gupta, Pratiksha Agarwal, Sukriti Verma, Balaji Krishnamurthy. 2017
44 citations recorded by the source corpus.
Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Przemyslaw Biecek
- Christoph Molnar
- Giuseppe Casalicchio
- Mateusz Staniak
- Bernd Bischl
- Alicja Gosiewska
- Anne-Laure Boulesteix
- Marco F. Huber
Citations in the corpus
- Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, neutral): “[168], andVarshney et al.”
- Explanations of model predictions with live and breakDown packages (2018, methodology): “Later, it was extended by Puri et al. (2017) to MAGIX methodology (Model Agnostic Globally Interpretable Explanations) and modified by the authors of the original article to aLIME (anchor-LIME) in Tulio Ribeiro et al. (2016a).”
- DALEX: explainers for complex predictive models (2018, background): “e introduced naturally in the modeling framework, see an example in Figure 1. In many areas the interpretability of a model is very important, see for example Lundberg and Lee (2017), Murad and Tarr, Puri (2017). The reason behind it that interpretability allows to clash the model structure with the”
- Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “Many IML methods are surrogate model approaches [89,75,72,95,34,10,18,61] and differ, e.”