1. A decision-tree system comprising: one or more processing modules; and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: storing structured data comprising a decision tree in a data store; storing a record to be analyzed according to the decision tree in an additional data store; storing one or more training records in a training data store; selecting, by a distinction module, between multiple paths extending from a node in the decision tree based at least in part on a unit of data from the record, the unit of data from the record carrying information relevant to a distinction of the node, the distinction module further comprising: a real-value module operable to make a first comparison between the unit of data from the record and a predetermined real value for the distinction of the node; and a set-value module operable to make a second comparison between the unit of data from the record and a predetermined set value for the distinction of the node, the set-value module is operable to make the second comparison by determining whether the unit of data from the record comprises an element of a set, the set defined by the predetermined set value for the distinction of the node, and the predetermined set value for the distinction of the node defines the set that comprises a unit of data with missing data; the distinction module further operable to select a path from the multiple paths based at least in part on at least one of: the first comparison; or the second comparison; accounting, by a training module operable to train the decision tree, for a known path of one of the one or more training records from the multiple paths extending from the node by both: a first relationship between a unit of training data from the one of the one or more training records and the predetermined real value for the distinction of the node; and a second relationship between the unit of training data and the predetermined set value for the distinction of the node; generating, by the training module, the decision tree in the data store as a probability estimation tree (PET) from the one or more training records by a machine learning algorithm; and using, by the machine learning algorithm, the missing data to: determine one or more nodes of the decision tree; or set the predetermined real value for the distinction of the node.