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This question examines some modifications on the loss function min - imization framework introduced in class, in the setting of binary classification. Specifically, we assume
This question examines some modifications on the loss function min
imization framework introduced in class, in the setting of binary classification. Specifically, we
assume that the features are collected in a vector x in Rp the dependent variable is binary
y in and our goal is to learn a function f : Rp R In order to use f to make
predictions, we adopt the prediction rule
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