Amazon technologies, inc. (20240185130). NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS simplified abstract
Contents
- 1 NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Simplified Explanation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Unanswered Questions
- 1.13 Original Abstract Submitted
NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Gowda Dayannda Anjaneyapura Range of Redmond WA (US)
Rajeev Ramnarain Rastogi of Bangalore (IN)
NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185130 titled 'NORMALIZING TEXT ATTRIBUTES FOR MACHINE LEARNING MODELS
Simplified Explanation
Simplified Explanation
The patent application involves computing correlation metrics between token groups in a dataset and a prediction target attribute. A predictive token group list is created based on these correlations, and values of a derived categorical attribute are determined for observation records. The predictive utility of the text attribute is measured using correlations between the categorical attribute and the prediction target attribute.
- Correlation metrics are computed between token groups and a prediction target attribute.
- A predictive token group list is created based on these correlations.
- Values of a derived categorical attribute are determined for observation records.
- The predictive utility of the text attribute is measured using correlations between the categorical attribute and the prediction target attribute.
Potential Applications
This technology could be applied in various fields such as data analysis, predictive modeling, and machine learning algorithms.
Problems Solved
This technology helps in identifying the predictive utility of text attributes in a dataset, allowing for more accurate predictions and insights.
Benefits
The technology enables better understanding of the relationship between text attributes and prediction targets, leading to improved predictive modeling and decision-making.
Commercial Applications
- Predictive analytics software
- Data mining tools
- Market research companies
Prior Art
There may be existing patents or research related to correlation analysis in datasets, but this specific approach may offer unique insights and applications.
Frequently Updated Research
There may be ongoing research in the field of predictive modeling and text analysis that could further enhance the capabilities of this technology.
Unanswered Questions
Question 1
How does this technology compare to traditional methods of correlation analysis in datasets?
Question 2
What are the potential limitations of using predictive token group lists in real-world applications?
Original Abstract Submitted
respective correlation metrics between token groups of a particular text attribute of a data set and a prediction target attribute are computed. based on the correlation metrics, a predictive token group list is created. for various observation records of the data set, values of a derived categorical attribute corresponding to the particular text attribute are determined based on matches between the particular text attribute value and the predictive token group list. a measure of the predictive utility of the particular text attribute is obtained using correlations between the categorical attribute and the prediction target attribute.
