Probability Vector

Probability vectors represent the likelihood of different outcomes or classes, serving as fundamental tools in various machine learning tasks, from classification and goal recognition to language modeling. Current research focuses on improving the accuracy and efficiency of probability vector estimation, particularly in imbalanced datasets and ensemble methods, often leveraging techniques like neural collapse and proper loss functions to optimize model performance. These advancements have significant implications for improving the reliability and robustness of machine learning models across diverse applications, including medical diagnosis and natural language processing.