2.2k citations · 2.8k across the 21 of their papers we have counts for
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch +1
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too of…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier +2
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for pre…