In the case of the instance above, the score is calculated as  The final score of is positive, so the instance is classified as spam. The magnitude of the value is a measure of confidence in the prediction. The original authors list three potential levels of interpretation for the set of attributes identified by an ADTree: Care must be taken when interpreting individual nodes as the scores reflect a re weighting of the data in each iteration. The inputs to the alternating decision tree algorithm are:  The fundamental element of the ADTree algorithm is the rule. A single rule consists of a precondition, a condition, and two scores. A condition is a predicate of the form "attribute  value." A precondition is simply a logical conjunction of conditions. Evaluation of a rule involves a pair of nested if statements:  Several auxiliary functions are also required by the algorithm:  The algorithm is as follows:  The set formula_14 grows by two preconditions in each iteration, and it is possible to derive the tree structure of a set of rules by making note of the precondition that is used in each successive rule. Figure 6 in the original paper demonstrates that ADTrees are typically as robust as boosted decision trees and boosted decision stumps. Typically, equivalent accuracy can be achieved with a much simpler tree structure than recursive partitioning algorithms.