US 12,314,290 B2
Key category identification and visualization
Xue Ying Zhang, Xi'an (CN); Si Er Han, Xi'an (CN); Jing Xu, Xi'an (CN); Xiao Ming Ma, Xi'an (CN); Wen Pei Yu, Xi'an (CN); Jing James Xu, Xi'an (CN); Jun Wang, Xi'an (CN); and Ji Hui Yang, Beijing (CN)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Jun. 12, 2023, as Appl. No. 18/333,510.
Prior Publication US 2024/0411783 A1, Dec. 12, 2024
Int. Cl. G06F 7/00 (2006.01); G06F 11/34 (2006.01); G06F 16/28 (2019.01)
CPC G06F 16/287 (2019.01) [G06F 11/3409 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method for treating post-modeling data, the method comprising:
selecting a category of a feature;
computing a first category importance (CI) value for the selected category of the feature, wherein the first CI value is based on a model accuracy change after reassigning records of the selected category to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include each category of the feature, except for the selected category; and
computing additional CI values for each category in the remaining set of categories by assigning each category as the selected category and determining the model accuracy change when records of the selected category are reassigned to the remaining set of categories of the feature according to the cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the selected category.