Classification Model

Classification models aim to assign data points to predefined categories, a fundamental task across numerous scientific disciplines and applications. Current research emphasizes improving model robustness and reliability, addressing issues like data imbalance, spurious correlations, and overconfidence, often through techniques such as hyperparameter optimization, data augmentation, and the use of architectures like Recurrent Neural Networks (RNNs), Random Forests, XGBoost, and Transformers. These advancements are crucial for enhancing the accuracy and trustworthiness of classification in diverse fields, from healthcare diagnostics and cybersecurity to environmental monitoring and social media analysis.