Machine Learning Interface for Variable Selection and Model Generation
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Solution Overview
Problem
Conventional machine learning requires complicated coding and specialized knowledge, making it not user-friendly for general users.
Innovation Solution
A machine learning system and method that includes a display device and a learning device, allowing users to execute machine learning with simple operations by selecting candidate variables, generating prediction models, and displaying results without coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional machine learning methods are used, then machine learning can be performed with high accuracy, but the operation becomes complicated and requires specialized knowledge
Solution Approach 1:
The patent introduces a machine learning execution support interface as an intermediary layer between the user and the machine learning framework. This interface provides a graphical user interface that automatically generates, manages, and executes machine learning models without requiring users to write or manage complex coding configurations, thereby resolving the contradiction between operational ease and system complexity
Solution Approach 2:
The system enables self-service by automatically handling machine learning tasks such as data preprocessing, model selection, training, and evaluation without requiring manual coding intervention. The graphical interface automatically manages the entire machine learning workflow, allowing users to perform analyses without specialized knowledge while maintaining high accuracy
2Reliability
If specialized machine learning knowledge is required, then accurate results can be achieved, but the system becomes less user-friendly
Solution Approach 1:
The graphical user interface serves as an intermediary that translates user-friendly selections into accurate machine learning computations. It automatically handles the complex mappings between user inputs and underlying computational operations, ensuring reliable results without requiring users to understand the technical complexity
Solution Approach 2:
The system provides automatic feedback by displaying machine learning results, determination coefficients, and model performance metrics directly in the graphical interface. This feedback mechanism allows users to interpret accurate results without needing to understand the computational processes, maintaining both reliability and user-friendliness
Data Source
AI summary
A machine learning system includes a display device and a learning device. The learning device causes a variable selection section in a learning execution screen of the display device to display a result of extracting candidate explanatory variables and candidate objective variables from a target file designated by a user. The learning device causes a result display section in the learning execution screen to display a result of analyzing, by machine learning, a relation between an explanatory variable and an objective variable selected by the user from among the candidate explanatory variables and the candidate objective variables displayed in the variable selection section.