ML Model Evaluation Metrics for Stochastic Spread Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning (ML) models for predicting stochastic spread events, such as natural disasters, are inefficient in resource consumption during evaluation, as traditional evaluation techniques require significant processing power and memory, and lack consideration for spatial spread characteristics.
Innovation Solution
Implementing a set of evaluation metrics, including a distance score, pyramiding density error, and min-max intersection over union (IOU), to evaluate ML models in a resource-efficient manner, accounting for stochastic spread by using augmented tree methods for distance computation and adjusting IOU calculations for continuous pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional evaluation techniques are used for ML models predicting stochastic spread events, then the evaluation can be performed, but the technical resources such as processing power and memory are consumed inefficiently
Solution Approach 1:
The patent segments the evaluation process into multiple hierarchical levels (coarse-grained to fine-grained) using a pyramid structure. Instead of evaluating all regions uniformly at high resolution, the system divides the spread event into hierarchical zones and applies different evaluation granularities to different zones, reducing overall computational resource consumption while maintaining evaluation accuracy for critical regions.
Solution Approach 2:
The patent applies local quality by using directionally-aware evaluation metrics that adapt to local spatial characteristics of the spread event. Different regions of the spread event are evaluated with metrics tailored to their specific directional patterns and spatial properties, rather than applying a uniform evaluation approach across the entire event, thereby improving evaluation efficiency by focusing computational resources where they are most needed.
2Measurement precision
If traditional evaluation techniques are used, then evaluation can be completed, but the accuracy of predictions accounting for spatial spread characteristics like scale and direction is insufficient
Solution Approach 1:
The patent introduces directional awareness as an additional dimension to the evaluation metrics. Instead of only evaluating spatial overlap in two dimensions, the system incorporates directional information by analyzing the orientation and directionality of spread patterns. This dimensional enhancement allows the metrics to capture both the extent and direction of spread events, improving prediction accuracy for stochastic spread characteristics.
Solution Approach 2:
The patent changes the parameters of evaluation metrics by introducing directionally-aware measurements that consider angular and orientational properties of spread events. The metrics transform from simple spatial overlap calculations to more sophisticated evaluations that incorporate directional vectors, spread rates, and spatial gradients, thereby improving the precision of predictions for stochastic spread events without requiring overly complex computational structures.
Data Source
AI summary
Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.