# Colloquium of the Department and the PhD Program

## title

*Building better data mining models with ROC analysis*

## speaker

## date & place

Wednesday, 25.01.2006, 16:15 h

Room C252

## abstract

Classification is a classical data mining problem where the
value of a discrete (often binary) dependent "class" variable
must be predicted from the values of the independent variables.
Many data mining models, such as naive Bayes and decision
trees, model a probability distribution over the dependent
variable from training data, and then predict whichever class
is estimated to have the highest probability. Receiver
Operating Characteristic analysis, a decision making model
originating from signal detection theory, can be used to
obtain more sophisticated decision rules which result in
better data mining models (or rather, which make better use
of the models). In this talk I will overview some of the
more innovative applications of ROC analysis in data mining.