Prediction Algorithm Performance Evaluation via Risk Metrics
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Solution Overview
Problem
Current methods for evaluating the performance of prediction algorithms trained using machine learning techniques are imprecise and do not account for all use cases, as they only provide partial answers and do not guarantee suitability for intended applications.
Innovation Solution
A method that involves obtaining data sets, calculating prediction precision using specific metrics, aggregating distributions, and applying risk metrics to assess the performance of prediction algorithms, including the use of generative models and introduction of imperfections to simulate real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional evaluation methods (robustness analysis or formal methods) are used, then the evaluation process is simple, but the measurement precision is insufficient and does not account for all use cases
Solution Approach 1:
The evaluation method segments the problem into multiple distinct use cases rather than treating evaluation as a single monolithic process. Each use case is evaluated separately with its own data generation and assessment procedures, allowing precise measurement for each specific application context while maintaining overall systematic control
Solution Approach 2:
The patent introduces a new dimension of evaluation by incorporating use case-specific probability distributions and generating synthetic data that reflects real-world operational conditions. This moves beyond traditional single-metric evaluation to a multi-dimensional assessment framework that considers precision, reliability, and applicability across different operational contexts
2Reliability
If the prediction algorithm is trained efficiently on the training data set, then the training performance is high, but the suitability for the intended use case is not guaranteed
Solution Approach 1:
The method performs preliminary actions by generating synthetic data sets that represent various possible use cases before actual deployment. These pre-generated data sets with known ground truths allow the algorithm to be evaluated under multiple hypothetical scenarios, ensuring reliability across different applications before real-world deployment
Solution Approach 2:
The patent changes key parameters by introducing use case-specific probability distributions and synthetic data generation parameters. By varying data characteristics, noise levels, and operational conditions across different synthetic data sets, the method assesses algorithm reliability under diverse parameter configurations rather than relying on a single training distribution
3Loss of information
If partial evaluation approaches are used, then the evaluation process is faster, but the answers are incomplete and imprecise
Solution Approach 1:
The method applies partial action selectively by focusing computational resources on generating and evaluating only the most relevant use case scenarios. Rather than exhaustively evaluating every possible scenario, the patent identifies and prioritizes critical use cases based on their probability and importance, achieving sufficient evaluation completeness without excessive time investment
Data Source
AI summary
The invention relates to a method for evaluating the performance of a prediction algorithm predicting the outputs for given inputs, the algorithm having been trained using a machine learning technique, the method including the steps of: obtaining data sets, each datum of a set corresponding to the outputs that the algorithm should give in the presence of the inputs of the set, receiving the probability that a set is observed, collecting the outputs predicted by the algorithm for each input of the data of the sets, determining the distribution of the prediction precision of the predicted output, aggregating the distributions determined by using an aggregation function using the probabilities received, and applying at least one risk metric to the aggregated distribution of prediction precision, for obtaining at least one indicator of the algorithm performance.