Confidence Scoring for ML Predictions Using Similar Data Points
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Solution Overview
Problem
Existing machine learning models struggle with uncertainty in predictions due to aleatoric and epistemic uncertainty, making it difficult for users to determine the reliability of predictions, which can have significant consequences in certain applications.
Innovation Solution
A confidence score model is trained to generate confidence scores for machine learning model predictions by analyzing the accuracy of predictions made by the model on similar data points and their distances in a multidimensional space, taking into account both aleatoric and epistemic uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model generates predictions without confidence scoring, then the prediction process is simple and fast, but the reliability and interpretability of predictions deteriorate
Solution Approach 1:
The solution segments the prediction system into two distinct components: the original machine learning model for generating predictions, and a separate confidence score model for estimating prediction reliability. This segmentation allows each component to specialize in its function while maintaining overall system manageability and interpretability.
Solution Approach 2:
The confidence score model acts as an intermediary between the machine learning model and the decision-making process. It provides an additional layer of information that mediates how predictions are interpreted and acted upon, without fundamentally changing the original prediction mechanism.
2Measurement precision
If a confidence score model is trained to account for aleatoric and epistemic uncertainty, then the measurement precision of prediction reliability improves, but the device complexity increases
Solution Approach 1:
The confidence score model utilizes changes in key parameters including the number of nearest neighbors (k), distance thresholds, and weighting factors to accurately capture both aleatoric and epistemic uncertainty. By systematically adjusting these parameters, the model achieves precise uncertainty measurement while maintaining computational efficiency.
Solution Approach 2:
The solution adds a new dimension to the prediction output by introducing confidence scores that operate in a separate dimensional space from the original predictions. This additional dimension provides information about prediction reliability without interfering with the primary prediction task.
3Measurement precision
If the confidence score model uses multiple nearest neighbors and distance calculations, then the accuracy of confidence scoring improves, but the loss of computational time increases
Solution Approach 1:
The model employs a selective approach by using a limited number of nearest neighbors (k) rather than calculating distances to all training data points. This partial action provides sufficient accuracy for confidence scoring while significantly reducing computational overhead compared to exhaustive methods.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing distance metrics during the training phase, and by pre-selecting the k nearest neighbors before generating confidence scores. This preliminary preparation reduces the computational burden during actual prediction and confidence scoring operations.
Data Source
AI summary
Techniques for generating confidence scores for machine learning predictions are disclosed. The confidence score for a predicted label corresponding to a target data point is based at least in part on how well the machine learning model predicts labels for other data points that are similar to the target data point. The system uses k data points, closest to the target data point, with known labels to compute the confidence score of a predicted label for the target data point. The accuracy of the predictions and the distance of each of the k data points from the target data point are used to compute a confidence score for a label predicted for the target data point.