Combined Confidence Metrics for Aggregated ML Predictions
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Solution Overview
Problem
Existing systems struggle to estimate confidence intervals for aggregated predictions from complex systems, leading to inefficiencies and inaccuracies, particularly in agricultural monitoring where false positives result in unnecessary resource wastage and reduced crop yields.
Innovation Solution
A method for generating combined confidence metrics using a computing system that processes intermediate outputs from machine learning models, employing deep learning architectures to determine the probability of correctness for final outputs, allowing for the rejection of predictions below a chosen threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual predictions from complex systems are aggregated, then the quantity of information increases, but the reliability of the aggregated prediction decreases due to inability to estimate confidence intervals
Solution Approach 1:
The patent introduces an intermediary confidence metric generation model that processes intermediate outputs from multiple machine learning models. This intermediary model aggregates confidence metrics from individual predictions and generates a combined confidence metric, serving as a mediator between individual predictions and final aggregated results. The intermediary model uses deep learning architectures to learn patterns in confidence aggregation without requiring explicit confidence interval estimation from each component model.
Solution Approach 2:
The patent replaces the mechanical approach of explicitly estimating confidence intervals from individual model outputs with a data-driven deep learning approach. Instead of using traditional statistical methods to combine confidence metrics, the system employs a neural network model that learns to aggregate confidence metrics through training on historical prediction data. This substitution transforms the confidence aggregation problem from a deterministic mathematical operation to a learned probabilistic process.
2Productivity
If complex systems with multiple intermediate outputs are used, then the productivity of the system increases, but the difficulty of detecting and measuring confidence metrics increases
Solution Approach 1:
The patent segments the confidence metric generation process into distinct components: individual machine learning models generate intermediate predictions with their own confidence metrics, these intermediate outputs are fed to a separate confidence metric generation model, which then produces the final combined confidence metric. This segmentation allows each component to specialize in its function while simplifying the overall measurement process. The intermediate outputs serve as structured inputs that bridge the gap between individual predictions and aggregated results.
Solution Approach 2:
The confidence metric generation model acts as an intermediary that receives intermediate outputs from multiple source models and produces a unified confidence metric. This intermediary structure simplifies the measurement process by abstracting away the complexity of aggregating multiple confidence sources. The intermediary model learns to weigh and combine intermediate confidence metrics based on patterns in the data, making the overall system more measurable and controllable.
Data Source
AI summary
A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. Outputs associated with combined confidence metrics that are below the threshold may be caused to be discarded.