The concept of bagging (voting for classification, averaging for regression-type problems with continuous dependent variables of interest) applies to the area of predictive data mining, to combine the predicted classifications (prediction) from multiple models, or from the same type of model for different learning data. It is also used to address the inherent instability of results when applying complex models to relatively small data sets.

Suppose your data mining task is to build a model for predictive classification,
and the data set from which to train the model (learning data set, which
contains observed classifications) is relatively small. You could repeatedly
sub-sample (with replacement) from the data set and apply, for example,
a tree classifier (e.g.,