Concept

Probabilistic classification

Summary
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles. Types of classification Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ŷ: :\hat{y} = f(x) The samples come from some set X (e.g., the set of all documents, or the set of all images), while the class labels form a finite set Y defined prior to training. Probabilistic classifiers generalize this notion of classifiers: instead of functions, they are conditional distributions \Pr(Y \vert X), meaning that for a given x \in X, they assign probabili
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