18182191. OPTIMIZATION FOR CASCADE MACHINE LEARNING MODELS simplified abstract (PayPal, Inc.)
Contents
OPTIMIZATION FOR CASCADE MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Nitin Satyanarayan Sharma of San Francisco CA (US)
Sanae Amani Geshnigani of San Jose CA (US)
OPTIMIZATION FOR CASCADE MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18182191 titled 'OPTIMIZATION FOR CASCADE MACHINE LEARNING MODELS
Methods and systems are presented for improving the accuracy, performance, and utilization rates of a cascade machine learning model system. The system includes multiple machine learning models that process transactions in a cascade operation scheme. Hyperparameter values are collectively determined to optimize the performance of the machine learning models within the cascade operation scheme. An efficacy determination model assesses the system's efficacy in processing transactions and modifies characteristics of the model based on the assessment.
- Multiple machine learning models in a cascade operation scheme
- Collective determination of hyperparameter values for optimization
- Efficacy determination model for assessing system performance
- Modification of model characteristics based on efficacy assessment
- Improved accuracy, performance, and utilization rates
Potential Applications: - Financial transactions processing - Fraud detection systems - Predictive maintenance in manufacturing - Healthcare diagnostics - Natural language processing
Problems Solved: - Enhancing accuracy and performance of machine learning models - Optimizing hyperparameter values for improved results - Real-time processing of transactions - Adapting model characteristics based on efficacy assessment
Benefits: - Increased accuracy in transaction processing - Enhanced performance of machine learning models - Improved utilization rates of the system - Adaptive and efficient processing of transactions
Commercial Applications: Title: Enhanced Machine Learning Model System for Transaction Processing This technology can be applied in various industries such as finance, healthcare, manufacturing, and more. It can significantly improve the efficiency and accuracy of transaction processing systems, leading to better decision-making and cost savings for businesses.
Questions about Cascade Machine Learning Model System: 1. How does the efficacy determination model impact the performance of the cascade machine learning model system? The efficacy determination model plays a crucial role in assessing the system's performance and making necessary modifications to enhance its efficiency.
2. What are the key benefits of using multiple machine learning models in a cascade operation scheme? Using multiple models in a cascade operation scheme allows for more comprehensive processing of transactions, leading to improved accuracy and performance.
Original Abstract Submitted
Methods and systems are presented for improving the accuracy performance and utilization rates of a cascade machine learning model system. The cascade machine learning model system includes multiple machine learning models configured to process transactions according to a cascade operation scheme. Hyperparameter values usable to configure the multiple machine learning models are determined collectively such that the hyperparameter values are selected to optimize the performance of the multiple machine learning models when the models operate according to the cascade operation scheme. Furthermore, an efficacy determination model is used to determine an efficacy of the cascade machine learning model in processing a given transaction. Based on an output of the efficacy determination model, one or more characteristics of the cascade machine learning model are modified for processing the transaction.
