US 12,249,012 B2
Visual representation using post modeling feature evaluation
Xiao Ming Ma, Xi'an (CN); Wen Pei Yu, Xian (CN); Jing James Xu, Xi'an (CN); Xue Ying Zhang, Xi'an (CN); Si Er Han, Xi'an (CN); Jing Xu, Xi'an (CN); and Jun Wang, Xi'an (CN)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Nov. 17, 2022, as Appl. No. 18/056,389.
Prior Publication US 2024/0169614 A1, May 23, 2024
Int. Cl. G06T 11/00 (2006.01); G06T 11/20 (2006.01)
CPC G06T 11/206 (2013.01) 20 Claims
OG exemplary drawing
 
1. A method for post-modeling feature evaluation, comprising:
obtaining at least one post model visual output and associated data, wherein said visual output includes at least an individual conditional expectation (ICE) plot and a partial dependence (PDP) plot; wherein an ICE plot provides a target value of a particular instance that corresponds to a change in a feature value, and a PDP plot provides a dependency correlation between one or more features on a target value;
using said associated data and said plots, providing a future importance (PI) plot; wherein said PI plot includes a plurality of input values indicating a relative contribution of a dataset on a future generated prediction;
determining a plurality of features for each PI, PDP and ICE plots to calculate at least one Interesting Value for each plot;
determining a turning point (TP) associated with the PDP and ICE plots, wherein said TP is identified by a starting point of a decrease or alternatively an increase on each of said PDP and ICE plots;
calculating an overall score for each plurality of features based and their associated Interesting Values for each PDP, ICE and PI plots and by values associated with the TP;
selecting at least one top feature based on said scores; and
generating a final plot by combining PI, PDP and ICE plots based on said at least one top feature.