Machine Learning Prediction Confidence From Nearest-Neighbor Accuracy
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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 system generates confidence scores for machine learning model predictions by analyzing the accuracy and distance of k nearest neighbors in a multidimensional space, taking into account both aleatoric and epistemic uncertainty, and adjusts model parameters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models generate predictions without confidence scoring, then prediction speed is maintained, but reliability assessment capability deteriorates
Solution Approach 1:
The system segments the prediction process into two independent components: the base machine learning model that generates predictions, and a separate confidence score model that evaluates prediction reliability. This segmentation allows each component to specialize in its function while maintaining overall system efficiency.
Solution Approach 2:
A confidence score model is introduced as an intermediary component between the base model and the decision-making process. This intermediary evaluates predictions by analyzing k-nearest neighbors and generates confidence scores without requiring fundamental changes to the base model architecture.
2Reliability
If confidence scores are generated for all predictions, then reliability assessment is improved, but computational overhead increases
Solution Approach 1:
The system implements a configurable parameter k that determines the number of nearest neighbors to analyze for confidence scoring. Users can adjust k to balance between computational efficiency and scoring accuracy, performing partial analysis when full analysis is unnecessary.
Solution Approach 2:
The system allows dynamic adjustment of parameters including k (number of neighbors), distance thresholds, and confidence score thresholds. These parameter changes enable the system to adapt computational effort based on specific application requirements and data characteristics.
3Measurement precision
If k-nearest neighbors analysis is performed with large k values, then confidence score accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The system uses a configurable k parameter that allows users to perform partial analysis by limiting the number of neighbors examined. This prevents excessive computation while maintaining sufficient accuracy for the specific application context.
Solution Approach 2:
The system pre-computes and stores distance metrics between data points during data processing phases. This preliminary action reduces the computational burden during actual prediction and confidence scoring operations, enabling faster processing without sacrificing accuracy.
Data Source
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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.