Auto-Query Generation for Machine Learning Systems
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Solution Overview
Problem
Current machine-learning 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 results with confidence levels, enabling novice users to derive predictions and optimizations.
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 in the system
Solution Approach 1:
The system enables self-service by automatically generating machine learning queries without requiring user expertise in query formulation. The automated query generation engine analyzes user inputs and autonomously constructs appropriate ML queries, eliminating the need for users to manually write complex query syntax while maintaining system sophistication through backend automation.
2Productivity
If automated query generation is implemented, then productivity increases by enabling novice users to execute ML analyses, but loss of information increases due to potential imprecise query interpretation
Solution Approach 1:
The system incorporates feedback mechanisms where the automated query generation engine continuously refines its query construction based on analysis of user inputs and system responses. This feedback loop allows the system to learn from interactions, improve query accuracy over time, and reduce information loss by better interpreting user intent while maintaining high productivity for novice users.
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.