Automated ML Query Generation via Symbolic Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine-learning (ML) techniques require substantial technical expertise, making it challenging for lay users to generate and develop queries effectively, often resulting in imprecise or unusable models due to improper query formatting.
Innovation Solution
An intuitive user interface and automated system that accepts input data to identify relationships between variables, generate machine learning problems, and formulate prediction or optimization queries, using symbolic regression and other algorithms to provide confident results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated query generation is implemented, then ease of operation improves for novice users, but device complexity increases due to symbolic regression algorithms and relationship determination mechanisms
Solution Approach 1:
The system introduces an automated query generation module as an intermediary between the user interface and the machine learning engine. This mediator automatically formulates queries based on user-selected parameters and training data, eliminating the need for users to manually construct complex queries while managing the complexity within the system architecture.
Solution Approach 2:
The system enables self-service by allowing the automated query generation mechanism to autonomously determine relationships between variables and formulate appropriate queries without human intervention. The symbolic regression algorithms automatically analyze training data and generate optimized queries, making the system self-sufficient in handling query complexity.
2Manufacturing precision
If manual query specification is required, then manufacturing precision of queries improves, but ease of operation deteriorates for lay persons
Solution Approach 1:
The system performs preliminary actions by pre-processing training data and pre-determining variable relationships before query execution. The automated generation process prepares optimized queries in advance based on analyzed patterns in the training data, ensuring high precision while requiring minimal user effort.
Solution Approach 2:
The system dynamically changes parameters by automatically adjusting query parameters based on the characteristics of the training data and the specific prediction task. The symbolic regression algorithms modify query structure, variable selections, and model complexity parameters to optimize both precision and ease of use for different scenarios.
Data Source
AI summary
Various systems and methods provide an intuitive user interface that enables automatic specification of queries and constraints for analysis by ML component. Various implementations provide methodologies for automatically formulating machine learning (“ML”) and optimization queries. The automatic generation of ML and/or optimization queries can be configured to use examples to facilitate formulation of ML and optimization queries. One example method includes accepting input data specifying variables and data values associated with the variables. Within the input data any unspecified data records are identified, and a relationship between the variables specified in the input data and a variable associated with the at least one unspecified data record is automatically determined. The relationship can be automatically determined based on training data contained within the input data. Once a relationship is established a ML problem can be automatically generated.