Google llc (20240202589). Transformation For Machine Learning Pre-Processing simplified abstract
Contents
- 1 Transformation For Machine Learning Pre-Processing
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Transformation For Machine Learning Pre-Processing - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 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 Questions about Machine Learning Pre-Processing
- 1.13 Original Abstract Submitted
Transformation For Machine Learning Pre-Processing
Organization Name
Inventor(s)
Jiaxun Wu of Sammamish WA (US)
Amir Hossein Hormati of Seattle WA (US)
Transformation For Machine Learning Pre-Processing - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202589 titled 'Transformation For Machine Learning Pre-Processing
Simplified Explanation
The patent application describes methods, systems, and apparatus for machine learning pre-processing. It involves creating a model, determining if a transform is needed, accessing training data, generating transformed training data, and storing metadata for the model.
Key Features and Innovation
- Creation of models for machine learning pre-processing
- Determination of the need for transforms in the model
- Generation of transformed training data using statistics on the training data
- Storage of metadata for the model including the transform and statistics
Potential Applications
This technology can be applied in various industries such as healthcare, finance, marketing, and more for improving machine learning models.
Problems Solved
This technology addresses the need for efficient pre-processing of data for machine learning models, ensuring better accuracy and performance.
Benefits
- Improved accuracy and performance of machine learning models
- Streamlined pre-processing of training data
- Enhanced data transformation capabilities
Commercial Applications
- Optimizing marketing campaigns through better predictive models
- Improving healthcare diagnostics with more accurate machine learning algorithms
- Enhancing financial risk assessment through advanced data processing techniques
Prior Art
Readers can explore prior research in the field of machine learning pre-processing, data transformation, and model creation to understand the evolution of this technology.
Frequently Updated Research
Stay updated on advancements in machine learning pre-processing, data transformation techniques, and model optimization to leverage the latest innovations in the field.
Questions about Machine Learning Pre-Processing
What are the key benefits of using transformed training data in machine learning models?
Transformed training data can help improve the accuracy and performance of machine learning models by providing a more refined input dataset.
How does storing metadata for models enhance the overall efficiency of machine learning processes?
Storing metadata for models allows for easy access to information about the transform and statistics used, facilitating better model management and optimization.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. in some implementations, an instruction to create a model is obtained. a determination is made whether the instruction specifies a transform. in response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. the training data is accessed. in response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. a model is generated with the transformed training data. a representation of the transform and the statistics is stored as metadata for the model.
