Evaluation of a binary classifier typically assigns a numerical value, or values, to a classifier that represent its accuracy.

Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade.