Model Vector Generation for Automated Machine Learning
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Solution Overview
Problem
Selecting and configuring machine learning algorithms to achieve desired outcomes within an acceptable time frame and resource consumption is challenging, leading to suboptimal performance in various applications.
Innovation Solution
A system that generates a model vector representing a weighted combination of machine learning models, adjusting weights and selecting feature and parameter subsets through iterative performance evaluations and meta-heuristic searches to optimize the model vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are selected and configured manually to achieve desired outcomes, then model performance can be optimized, but the time required and resource consumption increase significantly
Solution Approach 1:
The system enables automated self-service for model selection and configuration. The computing device automatically selects from a pool of machine learning models and configures their parameters without requiring manual human intervention, thereby reducing time loss while maintaining optimized performance through algorithmic evaluation and selection processes
Solution Approach 2:
The system automatically adjusts and optimizes model parameters through computational methods. By systematically varying and evaluating different parameter configurations across multiple models, the system identifies optimal settings that achieve desired performance outcomes while minimizing the time and resources required compared to manual configuration
2Adaptability or versatility
If a pool of diverse machine learning models is maintained for different use cases, then adaptability to various applications is improved, but device complexity increases
Solution Approach 1:
The system implements a universal model pool architecture where a single infrastructure supports multiple machine learning models with different functions and use cases. This universal platform manages diverse models through standardized interfaces and automated selection processes, achieving high adaptability across applications while containing complexity through systematic organization and automated management mechanisms
3Measurement precision
If iterative weight adjustment and feature selection processes are performed to optimize model vectors, then model accuracy is improved, but computational resources and time consumption increase
Solution Approach 1:
The system performs iterative weight adjustment and feature selection processes selectively rather than exhaustively. By applying optimization iterations only when necessary and stopping when performance thresholds are met, the system achieves improved model accuracy while avoiding unnecessary computational resource consumption from excessive iterations
Solution Approach 2:
The system implements feedback mechanisms that monitor model performance during iterative optimization processes. Based on performance feedback, the system dynamically adjusts the number and intensity of optimization iterations, allocating computational resources efficiently to achieve desired accuracy levels without wasting resources on unnecessary iterations
Data Source
AI summary
Techniques are described for forming a machine learning model vector, or just model vector, that represents a weighted combination of machine learning models, each associated with a corresponding feature set and parameterized by corresponding model parameters. A model vector generator generates such a model vector for executing automated machine learning with respect to historical data, including generating the model vector through an iterative selection of values for a feature vector, a weighted model vector, and a parameter vector that comprise the model vector. Accordingly, the various benefits of known and future machine learning algorithms are provided in a fast, effective, and efficient manner, which is highly adaptable to many different types of use cases.