The Partial Response Network: a neural network nomogram
Paulo J. G. Lisboa, Sandra Ortega-Martorell, Sadie Cashman, and Ivan, Olier

TL;DR
The paper introduces the Partial Response Network (PRN), a fully interpretable neural network model that combines the transparency of logistic regression with the ability to model non-linear effects, achieving competitive performance on benchmark data.
Contribution
It presents a novel method to derive a fully interpretable neural network from a generic MLP using ANOVA decomposition and feature selection, enabling non-linear classification with transparency.
Findings
PRN achieves comparable or superior performance to MLP and other ML methods.
PRN offers full interpretability similar to logistic regression.
PRN performs well on benchmark datasets against state-of-the-art models.
Abstract
Among interpretable machine learning methods, the class of Generalised Additive Neural Networks (GANNs) is referred to as Self-Explaining Neural Networks (SENN) because of the linear dependence on explicit functions of the inputs. In binary classification this shows the precise weight that each input contributes towards the logit. The nomogram is a graphical representation of these weights. We show that functions of individual and pairs of variables can be derived from a functional Analysis of Variance (ANOVA) representation, enabling an efficient feature selection to be carried by application of the logistic Lasso. This process infers the structure of GANNs which otherwise needs to be predefined. As this method is particularly suited for tabular data, it starts by fitting a generic flexible model, in this case a Multi-layer Perceptron (MLP) to which the ANOVA decomposition is applied.…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Neural Networks and Applications · Machine Learning and Data Classification
MethodsFeature Selection · Generalized additive models
