System and method for using classification trees to predict rare events

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Systems and methods are provided for predicting rare events, such as hospitalization events. A set of data records, each containing multiple attributes with one or more values (which may include an "unknown" value), may represent a root node of a decision tree. This root node may be partitioned based on one of the attributes, such that the concentration (e.g., "purity") of a relevant outcome (e.g., the rare event) is increased in one node and decreased in another. This process may be repeated until a decision tree with sufficiently pure leaf nodes is created. This "purified" decision tree may then be used to predict one or more rare events.