Machine Learning Interpretability with H 2 O Driverless

By Navdeep Gill, Megan Kurka, Wen Phan. 2017

28 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Giuseppe Casalicchio
2. Christoph Molnar
3. Marco F. Huber
4. Bernd Bischl
5. Nadia Burkart
6. Philipp Probst
7. Marc-André Zöller
8. Przemyslaw Biecek

Citations in the corpus

• Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (2021, unknown): “Hall et al. [14] proposed an approach to partition the dataset using k-means instead of perturbing the data points around an instance being explained.”
• Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, background): “IML has also arrived in industry [36], there are startups that focus on ML interpretability and also big tech companies offer software [126,8,43].”
• A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “RF Feature Importance (Hall et al., 2017a) is limited to decision trees and ensembles of decision trees.”