AI Data Processing System for ML Model Accuracy via Sampling
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Solution Overview
Problem
Machine learning models trained with incomplete or poor-quality data sets produce inaccurate results, and there is a need for techniques to improve the accuracy of these models, especially when important ranges in user preferences are poorly represented, and to enhance the relevance of input variable data sets.
Innovation Solution
A computer-implemented method that involves loading a training data set with multiple bins, applying under-sampling and over-sampling techniques, merging the updated bins, and training the machine learning model. This method also includes generating input variables by converting alphanumeric strings to scalar values, performing frequency filtering, and automatically determining optimal hyperparameters for the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained with incomplete or poor-quality data sets, then the training process can be completed more quickly, but the accuracy and reliability of the model results deteriorates
Solution Approach 1:
The system performs preliminary data quality assessment and filtering before training begins. It identifies and removes incomplete or poor-quality data records in advance, ensuring that only high-quality data is used for training, thereby improving model accuracy without significantly increasing training time.
Solution Approach 2:
The system implements feedback mechanisms during training to continuously monitor data quality and model performance. It uses this feedback to dynamically adjust the training process, selecting and weighting high-quality data samples to maximize model accuracy while maintaining efficient training duration.
2Reliability
If the training data set includes all available data, then the model can learn from more examples, but the computational resources and processing time required increase significantly
Solution Approach 1:
The system extracts and selects only the most relevant and high-quality data samples from the complete data set for training. It uses data filtering and sampling techniques to identify valuable data points that provide the most learning value, thereby reducing computational resource requirements while maintaining high model accuracy.
Solution Approach 2:
The system changes parameters related to data selection and training configuration to optimize the balance between model accuracy and computational resource consumption. It adjusts sampling rates, data filtering thresholds, and training hyperparameters to achieve efficient use of computational resources without sacrificing model performance.
3Reliability
If automated techniques are applied to improve input variable relevance, then the quality of input data can be enhanced, but the system complexity increases
Solution Approach 1:
The system implements self-service automated techniques that automatically assess, filter, and optimize input data quality without requiring manual intervention. It uses built-in algorithms to autonomously identify and correct data quality issues, enhancing input data quality while keeping the system operationally simple despite the increased underlying complexity.
Data Source
AI summary
A system includes memory hardware configured to store instructions, and processing hardware configured to execute the instructions. The instructions include training a machine learning model by loading a training data set, the training data set including a first bin and a second bin, applying an under-sampling technique to elements of the first bin to generate an updated first bin, applying an over-sampling technique to elements of the second bin to generate an updated second bin, generating an updated training data set by merging the updated first bin and the updated second bin, and training the machine learning model with the updated training data set, and providing the input variables to the trained machine learning model to generate output variables.