# A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery
###### Abstract  
Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the patient rule-induction method (PRIM), a subgroup-discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our innovation transforms PRIM into an interpretable classifier by starting with random input space selections for each class, then pruning rules using metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as random forest, logistic regression, and XG-Boost, our random PRIM-based classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it especially valuable in fields where understanding the model's decision-making process is as important as its predictive accuracy. classification; subgroup discovery; patient rule-induction method; metarules; interpretability +
Footnote †: journal: _Algorithms_  
0000-0001-1709-22000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-000  
logistic regression [11] and decision trees [12], which seek to develop a global predictive model for the complete dataset. In the data, it looks for particular patterns or areas that offer significant insights; these are frequently stated as straightforward rules, for example, \"age > 40 AND income < $50,000\". While logistic regression focuses on modeling relationships between variables using a single equation, frequently at the expense of interpretability, decision trees prioritize overall classification accuracy and may overlook smaller meaningful subgroups due to their hierarchical splitting process [13; 14]. Contrarily, subgroup discovery concentrates on identifying statistically significant and actionable patterns within particular data subsets [15]. This makes it especially useful for fields where it is crucial to comprehend and explain local patterns, such as marketing, policy analysis, and healthcare. In this context, the patient rule-induction method (PRIM) emerges as a promising tool. Developed by [PERSON] and [PERSON] [9], PRIM is a well-regarded algorithm for subgroup discovery, as stated in [16], known for its ability to identify and describe subgroups with distinct characteristics. These regions have rectangular shapes; hence, they are called \"boxes\". PRIM is highly valued for its ability to generate actionable insights through interpretable rules, making it a valuable tool in various fields, including medicine, finance, and marketing. However, while PRIM excels in revealing valuable subgroups, it is not inherently designed as a classifier. This limitation presents an opportunity to enhance PRIM's utility by developing a classifier based on its principles. In this paper, we propose a novel approach that leverages the strengths of PRIM to build a robust bump-hunting rule-based classifier. By integrating PRIM's rule-discovery capabilities with classification techniques, we aim to create a model that not only provides accurate predictions but also offers interpretable and actionable insights into the data. This advancement promises to bridge the gap between high accuracy and interpretability, ultimately contributing to more effective and trustworthy machine-learning applications.