Probabilistic Classification
Probabilistic Classification is a machine learning approach that predicts the likelihood of an input belonging to one or more classes rather than producing only a fixed class label.
What is Probabilistic Classification?
In probabilistic classification, a model assigns probabilities to possible classes based on the input data. For example, a model might predict that an email has an 85% probability of being spam and a 15% probability of being legitimate. The class with the highest probability can then be selected as the final prediction.
Why is Probabilistic Classification Important?
Probabilistic outputs provide more information than a simple class label. They allow teams to adjust classification thresholds, assess prediction confidence, and make decisions based on risk. Probability estimates can also support model calibration and uncertainty analysis.
Common use cases
Probabilistic Classification is commonly used in spam detection, fraud detection, medical diagnosis, sentiment analysis, risk assessment, and content moderation.