Method and apparatus for presenting feature importance in predictive modeling
US7561158B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 11, 2006 |
| Grant date | Jul 14, 2009 |
| Priority date | — |
| Expiry date | Aug 18, 2026 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T11/206
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.