US Patent Application 17736613. FEATURE SELECTION METHOD AND SYSTEM FOR REGRESSION ANALYSIS / MODEL CONSTRUCTION simplified abstract
FEATURE SELECTION METHOD AND SYSTEM FOR REGRESSION ANALYSIS / MODEL CONSTRUCTION
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Vladimir Sevastyanov of Fort Worth TX (US)
Abhay Dabholkar of Allen TX (US)
James H. Pratt of Round Rock TX (US)
Nikhlesh Agrawal of McKinney TX (US)
FEATURE SELECTION METHOD AND SYSTEM FOR REGRESSION ANALYSIS / MODEL CONSTRUCTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17736613 titled 'FEATURE SELECTION METHOD AND SYSTEM FOR REGRESSION ANALYSIS / MODEL CONSTRUCTION
Simplified Explanation
The patent application describes a method for determining the significance of a feature in relation to a target variable. Here are the key points:
- The feature range is divided into multiple subsets.
- The average target variable value is calculated for each subset.
- The difference between the maximum and minimum average target variable values is used to estimate the measure of feature significance.
- This method helps in understanding the importance of a feature in predicting the target variable.
- The patent application also mentions that there are other embodiments of this method.
Original Abstract Submitted
Aspects of the subject disclosure may include, for example, dividing a feature range of a feature into a plurality of subsets that span the feature range, calculating an average target variable value for each subset of the plurality of subsets, resulting in a plurality of average target variable values, and estimating a measure of feature significance with respect to a target variable by determining a difference between a maximum average target variable value and a minimum average target variable value in the plurality of average target variable values. Other embodiments are disclosed.
