System and method of training machine learning models to generate intuitive probabilities
US11386357B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 12, 2020 |
| Grant date | Jul 12, 2022 |
| Priority date | — |
| Expiry date | Mar 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for training a machine learning (ML) model for predicting probabilities for binary outcomes to automatically generate positive class predictions close to an ideal one probability and negative class predictions close to an ideal zero probability are disclosed. The method includes generating a predictive probability (PP) curve based on a ML algorithm and transforming the PP curve into a curve with probabilities spread close to ideal one probability for positive class predictions (PCP) indicating successful prediction and close to ideal zero probability for negative class predictions (NCP) indicating failed prediction, thereby introducing a valley in the transformed probability curve separating PCP from NCP. The PP curve is transformed by one of (1) minimizing distance between: (a) ideal one probability and PP value of PCP; and (b) ideal zero probability and PP value of NCP, and (2) maximizing distance of PP values from center of PP curve.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.