Discriminative Model

A discriminative model is a type of machine learning model that learns to distinguish between different classes or outcomes by modeling the relationship between input features and target labels. It focuses on predicting the probability of a specific outcome given observed data, rather than modeling how the data is generated. Discriminative models are widely used for tasks such as classification, regression, and risk scoring. In enterprise applications, they support use cases including fraud detection, demand forecasting, and customer segmentation. By directly optimizing predictive accuracy, discriminative models enable reliable, data-driven decision-making across business processes.