Multinomial Logistic

Multinomial logistic regression (MLR) is a statistical model used for multi-class classification problems, extending binary logistic regression to handle more than two outcome categories. Current research focuses on enhancing MLR's efficiency and applicability, including extensions to non-Euclidean spaces (e.g., Riemannian manifolds) and integration with other techniques like neural networks, active learning, and reinforcement learning algorithms. These advancements improve MLR's performance in diverse applications, ranging from economic forecasting and healthcare analysis to natural language processing and cybersecurity, offering more accurate and interpretable models for complex classification tasks.