Binary Classifier

Binary classifiers, which categorize data into two distinct classes, are a fundamental tool in machine learning, with research focusing on improving accuracy, addressing class imbalance, and ensuring fairness. Current efforts explore various model architectures, including support vector machines, neural networks (like multilayer perceptrons), and ensemble methods like gradient boosting, often coupled with techniques like SMOTE for handling imbalanced datasets and isotonic regression for calibration. The widespread applicability of binary classifiers spans diverse fields, from medical diagnosis and fraud detection to social media analysis and industrial anomaly detection, highlighting their significant impact on both scientific understanding and practical applications.