Auc Logistic Regression. Log px 1 px β0 β1x1 β2x2 βpxp. The Area Under the ROC curve AUC is an aggregated metric that evaluates how well a logistic regression model classifies positive and negative outcomes at all possible cutoffs.
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Typically assuming you used a default setup the AIC for logistic regression refers to the special case when both mis-classifications are equally costly. The higher the AUC the better the model is at correctly classifying outcomes. Mar 23 2020 The AUC area under curve gives us an idea of how well the model is able to distinguish between positive and negative outcomes.
Px P Y 1 X x p x P Y 1 X x we turn to logistic regression.
Typically assuming you used a default setup the AIC for logistic regression refers to the special case when both mis-classifications are equally costly. The Area Under the ROC curve AUC is an aggregated metric that evaluates how well a logistic regression model classifies positive and negative outcomes at all possible cutoffs. The AUC can range from 0 to 1. A model that returns probability of 08 for a particular patient that means the patient is more likely to have malignant breast cancer.
