A Paradigm for Potential Model Performance Improvement in Classification and Regression Problems. A Proof of Concept

Binary classification, multilabel classification, and regression prediction constitute fundamental paradigms in machine learning, addressing distinct types of predictive modeling tasks. Binary classification involves categorizing instances into one of two classes, typically denoted as positive and negative [1][2][3]. This modeling framework is particularly applicable to scenarios where outcomes are binary in nature, as observed in domains such as spam detection and medical diagnosis. In multilabel classification, the scope extends to situations where instances can be associated with multiple classes simultaneously, a common occurrence in applications like image tagging and document categorization [1][4]. Conversely, regression prediction is concerned with forecasting continuous outcomes, aiming to predict numeric values [3].