Measuring Latent Traits of Instance Hardness and Classifier Ability using Boltzmann Machines

Traditional Machine Learning (ML) approaches often emphasize evaluating models using global metrics over a dataset, frequently overlooking the nuances of learning data. Analyzing how hard it is to classify each instance, also known as instance hardness, furnishes such information, offering insights into reasons behind particular misclassifications. This paper introduces an unsupervised Deep Boltzmann Machine model integrated with an interpretability module that provides various latent traits related to instance hardness and classifier predictive performance. Such knowledge can facilitate in-depth analyses of the learning dataset's instances and the predictive power or ability of ML algorithms. Herein, we illustrate our approach by assessing five datasets with over 230 learning algorithms.