Binary Classification Uncertainty Quantification Using Beta Distributions
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Solution Overview
Problem
Existing binary classification models struggle to quantify uncertainty in their predictions, leading to difficulties in risk-informed decision-making and unreliable outcomes due to uncertainty in probability determinations.
Innovation Solution
A method involving multiple machine learning models to generate sample predictions, fitting a beta distribution to these predictions, and estimating hyperparameters to output a complete probability distribution for the binary class prediction, allowing for uncertainty quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a binary classification model determines a probability for positive class, then the model can provide class labels for decision-making, but the model cannot quantify its uncertainty in the probability determination
Solution Approach 1:
The patent extends the output from a single probability value to a complete probability distribution by adding an uncertainty dimension. Instead of only providing P(class=positive), the system provides a distribution over possible probability values, enabling both classification and uncertainty quantification simultaneously.
Solution Approach 2:
The patent changes the parameter representation from a point estimate (single probability value) to a distributional parameter (beta distribution with shape parameters α and β). This transformation allows the model to capture uncertainty through the distribution's characteristics while maintaining the ability to provide concrete probability estimates when needed.
2Ease of operation
If the model outputs a probability prediction, then decision-making can be performed, but risk assessment is insufficient without uncertainty quantification
Solution Approach 1:
The patent segments the probability output into two independent components: the mean probability (for decision-making) and the uncertainty distribution (for risk assessment). This segmentation allows decision-makers to access both the predicted probability and the associated uncertainty independently, improving both ease of operation and risk assessment capability.
Solution Approach 2:
The beta distribution serves as an intermediary that bridges the gap between the model's probability prediction and the decision-maker's risk assessment needs. It translates the model's internal probability estimates into a user-friendly distribution that provides both point estimates and uncertainty measures simultaneously.
3Measurement precision
If multiple machine learning models are used to generate sample predictions, then uncertainty can be quantified through beta distribution fitting, but the computational complexity increases
Solution Approach 1:
The patent uses multiple copies of the same or different machine learning models to generate ensemble predictions. By fitting a beta distribution to these replicated predictions, the system achieves precise uncertainty measurement through the variability among copies without requiring complex computational procedures.
Solution Approach 2:
The patent transforms the computational task from calculating complex uncertainty metrics directly to estimating beta distribution parameters (α and β) from sample predictions. This parameter transformation simplifies the computation while maintaining measurement precision, as the beta parameters can be estimated efficiently from the ensemble predictions.
Data Source
AI summary
Certain aspects of the disclosure provide systems and methods for uncertainty quantification of binary classification models. A method may include generating a plurality of sample predictions with a plurality of machine learning models, where each respective sample prediction of the plurality of sample predictions is associated with a respective model of the plurality of machine learning models. A probability distribution is fitted to the plurality of sample predictions. A classification label is determined based on the probability distribution.