Binary classification
نویسنده
چکیده
The problem of binary classification can be stated as follows. We have a random couple Z = (X ,Y ), where X ∈ Rd is called the feature vector and Y ∈ {−1,1} is called the label1. In the spirit of the modelfree framework, we assume that the relationship between the features and the labels is stochastic and described by an unknown probability distribution P ∈P (Z), where Z=Rd × {−1,1}. As usual, we consider the case when we are given an i.i.d. sample of length n from P . The goal is to learn a classifier, i.e., a mapping g :Rd → {−1,1}, such that the probability of classification error,P(g (X ) 6= Y ), is small. As we have seen before, the optimal choice is the Bayes classifier
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