# US 2025/0094831 A1 · Post-modeling category merging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0094831 A1 - Document ID: US20250094831A1 - Status: Published application - Kind: A1 - Application number: 18/370,613 - Filed: 2023-09-20 - Published: 2025-03-20 - Earliest claimed: 2023-09-20 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Xue Ying Zhang; Si Er Han; Xiao Ming Ma; Jing Xu - Web page: https://patentgrep.com/patent/US20250094831A1 ## CPC - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract An embodiment identifies, by a post-modeling category merging engine, a plurality of valid pairs associated with a categorical predictor, the plurality of valid pairs representing potential mergers of categories associated with a categorical predictor of a predictive model. The embodiment tests, by the post-modeling category merging engine, a merge strategy for the plurality of valid pairs to determine a merger that minimizes a loss in accuracy of the predictive model. The embodiment merges, by the post-modeling category merging engine based on the testing, a valid pair in the plurality of valid pairs to form a hybrid category. ## Claims ### Claim 1 A computer-implemented method comprising: identifying, by a post-modeling category merging engine, a plurality of valid pairs associated with a categorical predictor, the plurality of valid pairs representing potential mergers of categories associated with a categorical predictor of a predictive model; testing, by the post-modeling category merging engine, a merge strategy for the plurality of valid pairs to determine a merger that minimizes a loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid pair in the plurality of valid pairs to form a hybrid category. ### Claim 2 (depends on 1) The method of claim 1, wherein identifying the plurality of valid pairs further comprises: identifying a plurality of idle categories representing underutilized categories associated with the categorical predictor; merging the plurality of idle categories; and generating, based on a plurality of category importance values associated with a plurality of non-idle categories, the plurality of valid pairs. ### Claim 3 (depends on 2) The method of claim 2, wherein identifying the plurality of idle categories further comprises: identifying a category as idle responsive to a determination that the category includes a zero count in a training dataset associated with the predictive model. ### Claim 4 (depends on 2) The method of claim 2, wherein generating the plurality of valid pairs based on the plurality of category importance values further comprises: determining whether the categorical predictor is ordinal; and identifying as a valid pair, responsive to a determination that the categorical predictor is ordinal, two adjacent categories having a category importance value below a predetermined threshold. ### Claim 5 (depends on 4) The method of claim 4, further comprising: identifying as a valid pair, responsive to a determination that the categorical predictor is not ordinal, any two categories having a category importance value below a predetermined threshold. ### Claim 6 (depends on 1) The method of claim 1, wherein testing the merge strategy to determine the merger that minimizes the loss in accuracy of the predictive model further comprises: computing a plurality of model accuracy changes for a plurality of categories associated with the categorical predictor; sorting the plurality of model accuracy changes based on change magnitude; identifying a minimum model accuracy change in the sorted plurality of model accuracy changes; and determining to merge two categories associated with the minimum model accuracy change. ### Claim 7 (depends on 6) The method of claim 6, wherein the minimum model accuracy change is associated with a minimal decrease in accuracy for the predictive model. ### Claim 8 (depends on 6) The method of claim 6, wherein the minimum model accuracy change is associated with a maximum increase in accuracy for the predictive model. ### Claim 9 (depends on 1) The method of claim 1, further comprising: identifying, by the post-modeling category merging engine, a plurality of hybrid valid pairs associated with the hybrid category of the categorical predictor; testing, by the post-modeling category merging engine, another merge strategy for the plurality of hybrid valid pairs to determine another merger that minimizes the loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid hybrid pair in the plurality of hybrid valid pairs to form another hybrid category. ### Claim 10 A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: identifying, by a post-modeling category merging engine, a plurality of valid pairs associated with a categorical predictor, the plurality of valid pairs representing potential mergers of categories associated with a categorical predictor of a predictive model; testing, by the post-modeling category merging engine, a merge strategy for the plurality of valid pairs to determine a merger that minimizes a loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid pair in the plurality of valid pairs to form a hybrid category. ### Claim 11 (depends on 10) The computer program product of claim 10, wherein identifying the plurality of valid pairs further comprises: identifying a plurality of idle categories representing underutilized categories associated with the categorical predictor merging the plurality of idle categories; and generating, based on a plurality of category importance values associated with a plurality of non-idle categories, the plurality of valid pairs. ### Claim 12 (depends on 11) The computer program product of claim 11, wherein identifying the plurality of idle categories further comprises: identifying a category as idle responsive to a determination that the category includes a zero count in a training dataset associated with the predictive model. ### Claim 13 (depends on 11) The computer program product of claim 11, wherein generating the plurality of valid pairs based on the plurality of category importance values further comprises: determining whether the categorical predictor is ordinal; and identifying as a valid pair, responsive to a determination that the categorical predictor is ordinal, two adjacent categories having a category importance value below a predetermined threshold. ### Claim 14 (depends on 13) The computer program product of claim 13, further comprising: identifying as a valid pair, responsive to a determination that the categorical predictor is not ordinal, any two categories having a category importance value below a predetermined threshold. ### Claim 15 (depends on 10) The computer program product of claim 10, wherein testing the merge strategy to determine the merger that minimizes the loss in accuracy of the predictive model further comprises: computing a plurality of model accuracy changes for a plurality of categories associated with the categorical predictor sorting the plurality of model accuracy changes based on change magnitude identifying a minimum model accuracy change in the sorted plurality of model accuracy changes; and determining to merge two categories associated with the minimum model accuracy change. ### Claim 16 (depends on 10) The computer program product of claim 10, further comprising: identifying, by the post-modeling category merging engine, a plurality of hybrid valid pairs associated with the hybrid category of the categorical predictor testing, by the post-modeling category merging engine, another merge strategy for the plurality of hybrid valid pairs to determine another merger that minimizes the loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid hybrid pair in the plurality of hybrid valid pairs to form another hybrid category. ### Claim 17 A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising: identifying, by a post-modeling category merging engine, a plurality of valid pairs associated with a categorical predictor, the plurality of valid pairs representing potential mergers of categories associated with a categorical predictor of a predictive model; testing, by the post-modeling category merging engine, a merge strategy for the plurality of valid pairs to determine a merger that minimizes a loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid pair in the plurality of valid pairs to form a hybrid category. ### Claim 18 (depends on 17) The computer system of claim 17, wherein identifying the plurality of valid pairs further comprises: identifying a plurality of idle categories representing underutilized categories associated with the categorical predictor merging the plurality of idle categories; and generating, based on a plurality of category importance values associated with a plurality of non-idle categories, the plurality of valid pairs. ### Claim 19 (depends on 18) The computer system of claim 18, wherein identifying the plurality of idle categories further comprises: identifying a category as idle responsive to a determination that the category includes a zero count in a training dataset associated with the predictive model. ### Claim 20 (depends on 17) The computer system of claim 17, further comprising: identifying, by the post-modeling category merging engine, a plurality of hybrid valid pairs associated with the hybrid category of the categorical predictor testing, by the post-modeling category merging engine, another merge strategy for the plurality of hybrid valid pairs to determine another merger that minimizes the loss in accuracy of the predictive model; and merging, by the post-modeling category merging engine based on the testing, a valid hybrid pair in the plurality of hybrid valid pairs to form another hybrid category.