Decision tree insight discovery

A computer program product for presenting insights into a classification tree, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code readable/executable by at least one processor to perform a method comprising:

• performing a grouping analysis to group leaf nodes of a classification tree into a significant group and an insignificant group;
• performing influential target category analysis to identify one or more influential target categories for the leaf nodes of the classification tree in the significant group;
• and
• presenting one or more insights into the classification tree based on the grouping analysis and the influential target category analysis.

A computer system for presenting insights into a regression tree, the computer system comprising:

• one or more processors;
• one or more computer-readable memories;
• one or more computer-readable tangible storage devices;
• program instructions stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories to:
• perform a grouping analysis to group leaf nodes of a regression tree into a high group and a low group;
• perform unusual node detection analysis to detect one or more outlier nodes in the high group and in the low group;
• and
• present one or more insights into the regression tree based on the grouping analysis and the unusual node detection analysis.

A computer program product for presenting insights into a regression tree, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code readable/executable by at least one processor to perform a method comprising:

• performing a grouping analysis to group leaf nodes of a regression tree into a high group and a low group;
• performing unusual node detection analysis to detect one or more outlier nodes in the high group and in the low group;
• and
• presenting one or more insights into the regression tree based on the grouping analysis and the unusual node detection analysis.