ROC Curve Computation via Probability Bins
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Solution Overview
Problem
Current methods for calculating receiver operating characteristic (ROC) curves for models predicting binary variables are inefficient, especially when dealing with large datasets distributed across computer grids, as they often require sorting and aggregating data, which can be time-consuming and memory-intensive.
Innovation Solution
A method that initializes true and false count values for each probability bin, increments counts based on event occurrences, and computes true and false positive rates without the need for sorting, allowing for efficient calculation of ROC curves by summing counts across nodes in a grid system, thereby eliminating the need for data aggregation and reducing computational time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sorting and aggregating data is used to calculate ROC curves, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent changes the computational parameters from sorted data aggregation to unsorted data processing using probability bins. Instead of sorting all observations by predicted probability and then aggregating, the method processes data in unsorted order by assigning each observation to a probability bin based on its predicted probability value, thereby eliminating the sorting step while maintaining calculation accuracy.
2Measurement precision
If sorting and aggregating data is used to calculate ROC curves, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent segments the continuous probability range into discrete probability bins. Each bin represents a range of predicted probabilities, and the method counts observations within each bin independently. This segmentation allows parallel processing across multiple nodes without requiring complex aggregation logic, as each node can process its local data segments independently and contribute to the final ROC curve.
Solution Approach 2:
The patent extracts the sorting operation from the ROC curve calculation process entirely. By using probability bins to group observations, the method removes the sorting step that would otherwise be required, thereby simplifying the computational process and eliminating the need for data aggregation across nodes.
3Measurement precision
If sorting and aggregating data is used to calculate ROC curves, then measurement precision is improved, but memory requirements worsen
Solution Approach 1:
The patent changes the data structure from storing sorted observation lists to using probability bin counters. Instead of maintaining sorted arrays of all observations in memory, the method uses compact bin count structures that only store the number of observations in each probability bin, dramatically reducing memory requirements while preserving the ability to calculate ROC curves accurately.
Data Source
AI summary
A method of determining a false and/or a true positive rate is provided. A true count value and a false count value are initialized for probability bins. For a plurality of records, a truth of event occurrence and a probability of occurrence are read; a probability bin that includes the probability of occurrence is determined; the true count value of the determined probability bin is incremented when the truth of event occurrence indicates true; and the false count value of the determined probability bin is incremented when the truth of event occurrence indicates false. A true positive rate and a false positive rate are computed for each probability bin based on the true count value, the false count value, a determined total number of true event occurrences, and a determined total number of false event occurrences.