ML Threshold Configuration for Imbalanced Data
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Solution Overview
Problem
Machine learning models trained on imbalanced data sets face challenges in determining optimal threshold values, leading to decreased precision and accuracy due to the high imbalance between classes, resulting in false positives and false negatives.
Innovation Solution
A method involving generating a randomly sampled data set to rebalance the distribution between majority and minority classes, creating an analysis plot such as a precision-recall-gain curve, and identifying a threshold based on the slope of a tangential line to optimize the tradeoff between precision and recall, using a binary search to efficiently find the optimal threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a threshold value is established for binary classification in imbalanced data sets, then the model can classify inputs into two categories, but the precision in classifying inputs decreases due to the high imbalance between classes
Solution Approach 1:
The patent applies preliminary action by performing random sampling of the imbalanced data set before threshold determination. This preprocessing step creates a balanced or reweighted data representation that enables more accurate threshold selection. The sampling is performed in advance to transform the data distribution, allowing the subsequent threshold determination to achieve higher precision without being constrained by the original class imbalance.
2Measurement precision
If a threshold value is selected to improve precision, then false positives and false negatives increase, but if a different threshold is selected to reduce false positives and false negatives, then precision decreases
Solution Approach 1:
The patent applies parameter changes by transforming the threshold determination problem from a single fixed value to a dynamic range of optimal thresholds. By using random sampling to generate multiple data representations and determining thresholds for each, the system identifies a range of thresholds that collectively optimize both precision and accuracy. This approach allows the model to select appropriate thresholds based on specific operational requirements within the identified optimal range.
3Device complexity
If traditional threshold determination methods are used on imbalanced data sets, then the model configuration is simple, but the model performance decreases
Solution Approach 1:
The patent maintains relative simplicity by applying preliminary random sampling followed by straightforward threshold determination on the sampled data. This approach avoids complex model architecture modifications or iterative retraining processes. The random sampling step preprocessing the data allows standard threshold determination algorithms to produce optimal results, keeping the overall configuration process accessible and computationally efficient while significantly improving model performance on imbalanced data sets.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for efficiently configuring a machine learning model. An example method generally includes generating a randomly sampled data set from a data set including a larger first set of samples associated with a first classification and a smaller second set of samples associated with a second classification. An analysis plot for the machine learning model is generated based on the randomly sampled data set. A point associated with an accuracy metric for the machine learning model is identified on the analysis plot based on a slope of a line tangential to the identified point and a value identifying a relative importance of precision to recall in the machine learning model. The machine learning model is configured with a threshold value between the first classification and the second classification based at least in part on the identified point on the analysis plot.