MISS: Multiclass Interpretable Scoring Systems
Michal K. Grzeszczyk Tomasz Trzciński Arkadiusz Sitek
In this work, we present a novel, machine-learning approach for constructing
Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven
methodology for generating single, sparse, and user-friendly scoring systems
for multiclass classification problems. Scoring systems are commonly utilized
as decision support models in healthcare, criminal justice, and other domains
where interpretability of predictions and ease of use are crucial. Prior
methods for data-driven scoring, such as SLIM (Supersparse Linear Integer
Model), were limited to binary classification tasks and extensions to
multiclass domains were primarily accomplished via one-versus-all-type
techniques. The scores produced by our method can be easily transformed into
class probabilities via the softmax function. We demonstrate techniques for
dimensionality reduction and heuristics that enhance the training efficiency
and decrease the optimality gap, a measure that can certify the optimality of
the model. Our approach has been extensively evaluated on datasets from various
domains, and the results indicate that it is competitive with other machine
learning models in terms of classification performance metrics and provides
well-calibrated class probabilities.