Classification Losses and Decision Thresholds
Defend a threshold using decision costs and explain why accuracy alone can reward a useless classifier.
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Defend a threshold using decision costs and explain why accuracy alone can reward a useless classifier.
Defend an OLS-versus-ridge choice on a tabular dataset, including a case where extrapolation is unsafe.
Deliver a dataset card and defend one ambiguous labeling or exclusion decision with its impact on the target population.
Defend a model and feature-set choice using held-out evidence, complexity, cost and the limitations of the selection procedure.
Construct and execute a nonconvex counterexample with different initializations, using its outcomes to delimit the convex convergence guarantee.
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