A model produces class scores or probabilities from features, and a decision rule maps them to labels. Training uses labeled examples, while evaluation considers class balance, thresholds, and the costs of different errors.
Classification is the task of assigning an input to one or more discrete categories.
A model produces class scores or probabilities from features, and a decision rule maps them to labels. Training uses labeled examples, while evaluation considers class balance, thresholds, and the costs of different errors.