Simple Classifier

Simple classifiers, aiming for accurate and efficient classification with minimal computational complexity, are a cornerstone of machine learning. Current research emphasizes improving their robustness to noisy data, imbalanced datasets, and distribution shifts, often employing techniques like data augmentation, ensemble methods (e.g., Random Forests, Gradient Boosting), and logistic regression coupled with embeddings from smaller language models. These advancements are crucial for deploying reliable classifiers in resource-constrained environments and for enhancing the interpretability and trustworthiness of AI systems across diverse applications, from medical diagnosis to fraud detection.