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logistic regression
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Why is accuracy a poor way to judge a classification model?
Why the share of correct labels misleads with rare outcomes, ignores probabilities and costs, and which measures (Brier score, log loss, AUC) to use instead.
Read the answer →What is AUC? The area under the ROC curve, explained
AUC is the area under the ROC curve: the chance a model ranks a random positive case above a random negative one. How to read, compute and report it.
Read the answer →Logit vs probit: what is the difference, and which should you use?
Logit and probit models give almost the same fitted probabilities. They differ in coefficient scale and interpretation. Here is how to choose.
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