MEE with Discrete Errors
In this chapter we turn our attention to classifiers with a discrete error variable, E = T - Z. The need to operate with discrete errors arises when classifiers only produce a discrete output, as for instance the univariate data splitters used by decision trees. For regression-like classifiers, producing Z as a thresholding of a continuous output, Z = θ(Y), such a need does not arise. The present analysis of MEE with discrete errors, besides complementing our understanding of EE-based classifiers will also serve to lay the foundations of EE-based decision trees later in the chapter.
KeywordsInformation Gain Gini Index Discrete Error Probability Mass Function Split Point
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