Method and apparatus for presenting feature importance in predictive modeling
First Claim
1. A method of displaying feature importance in predictive modeling comprising the steps of:
- using a computer system having network connectivity to call a regression engine on a set of training data obtained from a storage unit connected to a computer network, said regression engine performing predictive modeling on said training data and outputting importance measures for explanatory variables for predicting a target variable;
calling a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and representing a feature correlation structure among said explanatory variables; and
displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables.
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Accused Products
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.
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Citations
15 Claims
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1. A method of displaying feature importance in predictive modeling comprising the steps of:
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using a computer system having network connectivity to call a regression engine on a set of training data obtained from a storage unit connected to a computer network, said regression engine performing predictive modeling on said training data and outputting importance measures for explanatory variables for predicting a target variable; calling a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and representing a feature correlation structure among said explanatory variables; and displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables. - View Dependent Claims (2, 3, 4, 5)
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6. A system for displaying feature importance in predictive modeling comprising:
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a regression engine operable on a set of training data that performs predictive modeling on the set of training data and outputs importance measures for explanatory variables for predicting a target variable; a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and represents a feature correlation structure among said explanatory variables; and a display for displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables. - View Dependent Claims (7, 8, 9, 10)
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11. A storage unit having computer code implementing a method of displaying feature importance in predictive modeling, said method comprising the steps of:
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calling a regression engine on a set of training data that performs predictive modeling on the set of training data and outputs importance measures for explanatory variables for predicting a target variable; calling a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and representing a feature correlation structure among said explanatory variables; and displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables. - View Dependent Claims (12, 13, 14, 15)
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Specification