Selecting an algorithm for analyzing a data set based on the distribution of the data set
US11568179B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 12, 2019 |
| Grant date | Jan 31, 2023 |
| Priority date | — |
| Expiry date | Nov 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A model analyzer may receive a representative data set as input and select one of a plurality of analytic models to perform the analysis. Before deciding which model to use the model may be trained, and the trained model evaluated for accuracy. However, some models are known to behave poorly when the training data is distributed in a particular way. Thus, the cost of training a model and evaluating the trained model can be avoided by first analyzing the distribution of the representative data. Identifying the representative data distribution allows ruling out use of models for which the distribution of the representative data is unsuitable. Only models that may be compatible with the distribution of the representative data may be trained and evaluated for accuracy. The most accurate trained model whose accuracy meets an accuracy threshold may be selected to analyze subsequently received data related to the representative data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.