Machine Learning Model Optimization via Prediction Slicing
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Solution Overview
Problem
Existing machine learning model optimization techniques struggle to identify and address specific issues affecting overall performance, as they rely on aggregate metrics rather than analyzing individual predictions or groups of predictions.
Innovation Solution
The system creates slices of predictions based on input feature vectors and determines optimization metrics such as accuracy, precision, and recall volume scores to pinpoint and optimize underperforming slices within the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If aggregate performance metrics are used to optimize the machine learning model, then the optimization process is simple and fast, but it is difficult to identify specific issues affecting model performance
Solution Approach 1:
The patent divides the model's predictions into multiple slices based on different facets (e.g., input features, prediction outcomes, data sources). Each slice represents a subset of predictions with specific characteristics. By analyzing performance metrics at the slice level rather than globally, the system can identify which specific segments are contributing to poor overall performance, thus resolving the contradiction between fast aggregate optimization and precise issue identification.
2Reliability
If global optimization techniques are applied to the machine learning model, then the overall model performance improves, but specific underperforming areas are overlooked
Solution Approach 1:
The patent introduces a new dimension of analysis by creating multiple slices of predictions based on different facets. Instead of viewing model performance as a single global metric, the system adds dimensional breakdowns (e.g., by input feature, by prediction class, by data source) that allow simultaneous assessment of both overall performance and local performance issues. This dimensional approach prevents loss of local information while maintaining global optimization.
3Measurement precision
If predictions are analyzed in detail to identify specific issues, then issue identification accuracy improves, but the optimization process becomes complex and time-consuming
Solution Approach 1:
The patent implements a hierarchical optimization approach where predictions are first analyzed at the slice level to identify problematic areas. Only slices with suboptimal performance metrics are selected for detailed analysis and optimization. This partial action approach focuses computational resources on specific underperforming segments rather than analyzing all predictions in detail, thus maintaining high issue identification accuracy while reducing overall process complexity.
Data Source
AI summary
A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.