Abstract: Provided are techniques for interactive adjustment of decision rules. A modified decision rule with one or more decision rule conditions is received for adjusting an original decision tree, wherein at least one of the decision rule conditions has been modified. A decision rule condition that has been modified and a corresponding decision tree node of the original decision tree are selected. Data records from a database are selected for a parent node of the corresponding decision tree node. The selected data records that match the decision rule condition are filtered. A sub-tree is generated using the filtered data records with a first splitting variable from the modified decision rule condition. An original sub-tree is replaced with the generated sub-tree and the decision rule condition to form an adjusted decision tree. The adjusted decision tree is used to predict a value of a target variable based on available predictors.

Abstract: Provided are techniques for interactive adjustment of decision rules. A modified decision rule with one or more decision rule conditions is received for adjusting an original decision tree, wherein at least one of the decision rule conditions has been modified. A decision rule condition that has been modified and a corresponding decision tree node of the original decision tree are selected. Data records from a database are selected for a parent node of the corresponding decision tree node. The selected data records that match the decision rule condition are filtered. A sub-tree is generated using the filtered data records with a first splitting variable from the modified decision rule condition. An original sub-tree is replaced with the generated sub-tree and the decision rule condition to form an adjusted decision tree. The adjusted decision tree is used to predict a value of a target variable based on available predictors.