Silhouettes and quasi residual plots for neural nets and tree-based classifiers

TL;DR

This paper introduces visualization techniques like silhouette and quasi residual plots to analyze and interpret neural network and tree-based classifiers by examining classification confidence and potential label bias.

Contribution

It proposes new visualization methods to analyze classifier decisions and label bias using PAC-based silhouette plots and residual plots, enhancing interpretability.

Findings

Silhouette plots help compare classification quality.

Quasi residual plots reveal data feature influences.

Visualizations applied to diverse benchmark datasets.

Abstract

Classification by neural nets and by tree-based methods are powerful tools of machine learning. There exist interesting visualizations of the inner workings of these and other classifiers. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to different class. This is reflected in the (conditional and posterior) probability of the alternative class (PAC). A high PAC indicates label bias, i.e. the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis (Rousseeuw, 1987). The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class. The graphical displays are illustrated and interpreted on benchmark data sets containing images, mixed features, and tweets.