Traditional decision tree induction, as epitomised by CART and
ID3/C4.5, do not employ any test of statistical significance in
deciding on which variables to choose when partitioning the data.
Conditional trees have been introduced to address this by using a
conditional distribution, measuring the association between the
output and the input variables.