Stacked Ensemble Model Dynamic Reliability Estimation
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Solution Overview
Problem
Estimating the reliability of machine-learned model predictions is challenging, as existing methods either fail to account for variations in prediction quality across different data instances or are computationally demanding, especially when using probabilistic models.
Innovation Solution
A prediction system that employs a stacked ensemble model, combining multiple base models to generate meta predictions and dynamically computes a reliability measure by obtaining a prior distribution and likelihood function, allowing for real-time adjustment of confidence intervals based on input data similarity to training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probabilistic machine-learned models are used to obtain confidence intervals from estimated distribution, then reliability estimation is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the reliability estimation process into two distinct phases: (1) training phase where the probabilistic model estimates the distribution and computes confidence intervals, and (2) inference phase where the pre-computed confidence intervals are directly applied without additional computational overhead. This segmentation allows the computationally intensive probabilistic modeling to be performed only during training, while inference remains efficient.
Solution Approach 2:
The patent applies preliminary action by pre-computing the confidence intervals and distribution estimates during the training phase before the actual inference is performed. The trained model stores these pre-computed reliability metrics, which are then directly retrieved and applied during inference without requiring real-time computational resources for distribution estimation.
2Device complexity
If performance metric evaluation is used during training, then reliability estimation is simplified, but prediction quality variations across different data instances are not accounted for
Solution Approach 1:
The patent implements dynamics by transitioning from a static performance metric (single average accuracy value) to a dynamic confidence interval that adapts to each prediction instance. The confidence interval is computed based on the local characteristics of the input data and the estimated distribution at that specific point, allowing the reliability measure to dynamically reflect the actual prediction quality for each data instance rather than using a fixed average metric.
Solution Approach 2:
The patent applies local quality by computing confidence intervals that are specific to each prediction instance rather than using a global average performance metric. The confidence interval for each prediction is derived from the local distribution estimate at that particular input point, capturing the local variation in prediction quality. This allows different regions of the input space to have different reliability measures reflecting their actual prediction accuracy.
Data Source
AI summary
A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.