Fast optimization of classification thresholds

Binary classification problems (target/non-target) are often modeled as a pair where is our model, which maps input vectors to scores, and is our threshold, such that we predict to be of target class iff . Otherwise, we predict it to be of non-target class. The threshold is usually set to , but this needs notContinue reading “Fast optimization of classification thresholds”

Average Precision is sensitive to class priors

Average Precision (AP) is an evaluation metric for ranking systems that’s often recommended for use with imbalanced binary classification problems, especially when the classification threshold (i.e. the minimum score to be considered a positive) is variable, or not yet known. When you use AP for classification you’re essentially trying to figure out whether a classifierContinue reading “Average Precision is sensitive to class priors”