ML Model Regressor Validation via Sparsity Classification
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Solution Overview
Problem
Machine learning models face accuracy issues when dealing with sparse data, leading to unreliable predictions, as insufficient data in certain categories results in unreliable regressors being output, which can mislead users in budgeting and decision-making.
Innovation Solution
A system that utilizes a recurrent neural network (RNN) within a machine learning model, incorporating a regressor layer and a classification layer, generates regressors and classifications based on time series datasets, identifying and preventing the output of invalid regressors by assessing their validity based on data sparsity, using a combined loss function for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model processes all time series datasets including sparse data, then the model covers more data categories, but the prediction accuracy decreases due to unreliable regressors from sparse data
Solution Approach 1:
The patent segments the output processing into two independent parts: a regressor layer that generates predictions and a classification layer that validates data quality. This segmentation allows the system to process all data categories while filtering out unreliable predictions from sparse data, resolving the contradiction between comprehensive coverage and prediction accuracy.
Solution Approach 2:
The classification layer acts as an intermediary between the regressor layer and the final output. It evaluates the sparsity of input data and validates regressors before they are presented to users, serving as a mediator that prevents unreliable predictions from reaching the user while maintaining comprehensive data processing.
2Productivity
If the model outputs all generated regressors, then the output volume is maximized, but users receive unreliable predictions from sparse data that can mislead decision-making
Solution Approach 1:
The patent converts the harmful effect of sparse data into a beneficial filtering mechanism. The classification layer detects sparse data patterns and uses them to identify and filter out unreliable regressors, transforming the potential harm of sparse data into a quality control mechanism that improves overall output reliability.
Solution Approach 2:
The classification layer provides feedback about data sparsity and regressor validity to the output generation process. This feedback mechanism allows the system to adjust which regressors are presented to users based on data quality assessments, preventing misleading predictions while maintaining high output volume from valid data.
3Device complexity
If the model processes sparse data without validation, then the processing complexity is minimized, but the system outputs unreliable regressors that reduce user trust
Solution Approach 1:
The patent merges the regressor generation function and the data validation function into a single integrated machine learning model. This merging allows the system to maintain relatively simple processing architecture while incorporating reliability validation, as both functions share the same model structure and can be trained together using a combined loss function.
Data Source
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AI summary
Systems and methods for generating regressors based on data sparsity using a machine learning (ML) model are described. A system is configured to provide a plurality of time series datasets to a recurrent neural network (RNN) of a machine learning (ML) model. The RNN generates one or more outputs associated with one or more time series datasets, and the system provides a first portion and a second portion of the one or more outputs to a regressor layer and a classification layer of the ML model, respectively. The regressor layer generates one or more regressors for the one or more time series datasets, and the classification layer generates one or more classifications associated with the one or more regressors (with each indicating whether an associated regressor is valid). Whether a classification indicates a regressor is valid may be based on time series data sparsity.