Decision lists are a representation for Boolean functions which can be easily learned from examples. Single term decision lists are more expressive than disjunctions and conjunctions; however, 1-term decision lists are less expressive than the general disjunctive normal form and the conjunctive normal form.
The language specified by a k-length decision list includes as a subset the language specified by a k-depth decision tree.
Learning decision lists can be used for attribute efficient learning, a type of machine learning.
A decision list (DL) of length is of the form:
if then output else if then output ... else if then output
where is the th formula and is the th boolean for . The last if-then-else is the default case, which means formula is always equal to true. A -DL is a decision list where all of formulas have at most terms. Sometimes "decision list" is used to refer to a 1-DL, where all of the formulas are either a variable or its negation.