# Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation Source: [Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:31:19.733Z Authors: Wojciech Samek, Gr\\'egoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert M\\"uller Published: 2016-11-24T14:16:12.000Z ## Abstract Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application. These models however lack transparency due to their complex nonlinear structure and to the complex data distributions to which they typically apply. As a result, it is difficult to fully characterize what makes these models reach a particular decision for a given input. This lack of transparency can be a drawback, especially in the context of sensitive applications such as medical analysis or security. In this short paper, we summarize a recent technique introduced by Bach et al. \[1\] that explains predictions by decomposing the classification decision of DNN models in terms of input variables. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()