15. A system, comprising: a datastore storing split-rules and an initial training set of data records used to generate the split rules, the initial training set of data records including negative training pairs that each include at least two data records that have not been identified as duplicate data records, each training pair having match scores specifying a measure of similarity for attributes of training pairs; and at least one processor of a model refinement system coupled to the datastore, the at least one processor configured to: remove removing at least one clause from the split rules to generate initial trimmed rules, the removing being based at least in part on a threshold match score specifying a match score at which the initial training set is segmented; classify the negative training pairs in the initial training set based on the match scores for the negative training pairs and the initial trimmed rules; remove, based on the classification, negative training pairs that are classified as duplicate pairs from the initial training set to create a filtered training set; generate an intermediate decision tree with the filtered training set, the intermediate decision tree defining intermediate split-rules; and generate final split rules based on the intermediate split rules, the final split rules including at least one final split rule that differs from each of the intermediate split rules.
15. A system, comprising: a datastore storing split-rules and an initial training set of data records used to generate the split rules, the initial training set of data records including negative training pairs that each include at least two data records that have not been identified as duplicate data records, each training pair having match scores specifying a measure of similarity for attributes of training pairs; and at least one processor of a model refinement system coupled to the datastore, the at least one processor configured to: remove removing at least one clause from the split rules to generate initial trimmed rules, the removing being based at least in part on a threshold match score specifying a match score at which the initial training set is segmented; classify the negative training pairs in the initial training set based on the match scores for the negative training pairs and the initial trimmed rules; remove, based on the classification, negative training pairs that are classified as duplicate pairs from the initial training set to create a filtered training set; generate an intermediate decision tree with the filtered training set, the intermediate decision tree defining intermediate split-rules; and generate final split rules based on the intermediate split rules, the final split rules including at least one final split rule that differs from each of the intermediate split rules. 17. The system of claim 15 , wherein the model refinement system is further configured to classify input pairs of data records as duplicate pairs based on attribute values of the input pairs and the final split-rules.