ML Model Accuracy via Statistical Baseline
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Solution Overview
Problem
Machine learning models can produce inaccurate results due to incorrect training data or learning from incorrect results, leading to adverse effects in various industries, and it is challenging to quickly detect when a machine learning model is diverging from optimal performance, resulting in resource-inefficient content delivery.
Innovation Solution
A system that balances content selection between machine learning and statistical models by assigning data requests to different response pools based on performance feedback, adjusting the allocation policy to favor the model with better performance, ensuring optimal content delivery by dynamically updating the distribution factor based on user acceptance rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used for content selection, then content delivery accuracy can be improved, but the system may diverge from optimal performance due to incorrect training data or learning from incorrect results
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors the performance of the machine learning model by comparing its content selections against ground truth data or alternative models. When the model's accuracy falls below a threshold, the system detects this divergence and triggers corrective actions such as retraining or switching to a statistical model, thus maintaining reliability while allowing the ML model to improve accuracy.
Solution Approach 2:
The system dynamically changes operational parameters by adjusting the distribution factor that determines the proportion of requests handled by the ML model versus a statistical model. When the ML model underperforms, the system reduces the parameter (distribution factor) allocating traffic to the ML model, thereby maintaining system reliability while preserving the potential for high accuracy when the model performs well.
2Measurement precision
If the system monitors machine learning model performance continuously, then accuracy can be maintained, but computational resources and time are consumed
Solution Approach 1:
The system performs partial monitoring by evaluating only a subset of content selections against ground truth data or alternative models, rather than continuously validating every prediction. This partial evaluation approach maintains sufficient accuracy monitoring while significantly reducing the time and computational resources required, allowing the system to balance precision with efficiency.
3Productivity
If the system switches between machine learning and statistical models, then optimal content delivery can be achieved, but system complexity increases
Solution Approach 1:
The system implements dynamic model selection where the architecture transitions from static to adaptive based on performance conditions. The distribution factor becomes a dynamic parameter that automatically adjusts the proportion of requests routed to ML versus statistical models based on real-time performance feedback, optimizing productivity while managing complexity through automated control rather than manual configuration.
Data Source
AI summary
Balancing content distribution between a machine learning model and a statistical model provides a baseline assurance in combination with the benefits of a well-trained machine learning model for content selection. In some implementations, a server receiving requests for a content item assigns a first proportion of the received requests to a first group and assigns remaining requests to a second group. The server uses a machine learning model to select variations of the requested content item for responding to requests assigned to the first group and uses a statistical model to select content variations for requests assigned to the second group. The server obtains performance information, e.g., acceptance rates for the different variations, and compares performance of the different models used for content selection. Audience share assigned to the machine learning model is increased when it outperforms the statistical model and decreased when it underperforms the statistical model.