Iterative Machine Learning Model Feedback Loop for Estimation Accuracy
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Solution Overview
Problem
Machine learning methods for predicting an objective variable from an explanatory variable face a plateau in accuracy.
Innovation Solution
A data estimation technique that creates a machine learning model to estimate an objective variable, adds the estimated objective variable back into the explanatory variable group, and iteratively builds subsequent models to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used to predict objective variables from explanatory variables, then the model can be trained and deployed, but the accuracy in objective variable estimation reaches a plateau and cannot be further improved
Solution Approach 1:
The patent applies feedback by taking the estimated objective variable from one model and feeding it back as an explanatory variable for training the next model. This creates a iterative loop where each model's output becomes the next model's input, continuously improving estimation accuracy by incorporating previously derived information into subsequent training processes.
Solution Approach 2:
The patent performs preliminary actions by first training a model to estimate objective variables before using those estimates as explanatory variables for subsequent models. This preparatory step ensures that each iterative cycle has the necessary preliminary information (estimated objective variables) ready to enhance the next model's training, breaking the accuracy plateau through structured preliminary processing.
2Measurement precision
If multiple machine learning models are trained iteratively with added objective variables, then estimation accuracy improves, but the training process becomes more complex and time-consuming
Solution Approach 1:
The patent maintains continuity of useful action by making each model's output directly useful for the next model's input without interruption or waste. The estimated objective variables are continuously generated and immediately utilized as explanatory variables in the next iteration, ensuring that each training cycle builds directly on the previous one without loss of useful information or unnecessary delays.
Data Source
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AI summary
Provided is a data estimation technique capable of improving accuracy in estimation of an objective variable from an explanatory variable. In a data estimation device (100), a learning unit (20) creates, using training data including an explanatory variable and an objective variable, a machine learning model that estimates an objective variable from an explanatory variable. The learning unit (20) creates a machine learning model Mi that estimates an objective variable Oi from an explanatory variable group Ei including one or more explanatory variables, sets a new explanatory variable group Ei + 1 by adding the objective variable Oi estimated by the machine learning model Mi to the explanatory variable group Ei, and creates a machine learning model Mi + 1 that estimates an objective variable Oi + 1 from the explanatory variable group Ei + 1 (where i = 1). The learning unit (20) repeatedly creates a machine learning model while i is in a range of from 2 to (n - 1) (n is a natural number greater than or equal to 2).