Probabilistic Classifier

Probabilistic classifiers aim to predict not only class labels but also the associated probabilities, providing a measure of uncertainty in predictions. Current research focuses on improving calibration (the accuracy of predicted probabilities), developing novel scoring rules for better model evaluation and selection, and exploring efficient algorithms like ridge regression as alternatives to computationally expensive methods such as logistic regression. These advancements are significant for enhancing the reliability and interpretability of machine learning models across diverse applications, from astrophysics to bioinformatics and risk assessment, where understanding uncertainty is crucial for informed decision-making.