Discriminative model

Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typically models the conditional distribution P(Y∣X), or it learns a direct decision rule that maps inputs X to outputs Y. Discriminative models are commonly used for classification and regression, where the main goal is accurate prediction on new data. They are typically used to solve binary classification problems, i.e. assign labels, such as pass/fail, win/lose, alive/dead or healthy/sick, to existing datapoints. Discriminative models are usually trained to separate classes or to minimize prediction error under a chosen loss function. They are often contrasted with generative models, which aim to model how the data are generated and can be used to sample new data.

Types of discriminative models include logistic regression (LR), conditional random fields (CRFs), decision trees among many others.