Machine Learning Probability Curve Transformation for Binary Classification
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Solution Overview
Problem
Standard machine learning algorithms often generate prediction probabilities that do not match the intuitive expectations of users, particularly for binary outcomes like success/failure, where probabilities are not close to ideal 1 or 0, requiring labor-intensive manual categorization or custom algorithm development for each domain, which is costly and impractical.
Innovation Solution
A system and method for training machine learning models to generate predictive probability curves that minimize the distance between ideal probabilities (1 for positive and 0 for negative class predictions) and maximize the distance of predicted probabilities from the center, introducing a valley in the transformed curve to separate positive and negative class predictions intuitively, using error metrics and statistical techniques to shift and normalize probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard machine learning algorithms are used to generate predictions, then the model can differentiate well between success and failure cases, but the predicted probabilities do not match the intuitive expectation of users (should be close to 0 or 1)
Solution Approach 1:
The patent transforms the probability distribution by applying a mathematical transformation function that maps the original probability values to new values. This transformation changes the parameter distribution of predictions, pushing successful cases closer to 1 and failed cases closer to 0, thereby resolving the contradiction between maintaining prediction accuracy and improving intuitiveness.
2Ease of operation
If manual categorization into probability buckets is implemented, then end users can easily understand predictions, but the process becomes extremely labor-intensive
Solution Approach 1:
The system automatically transforms probability values through mathematical functions without requiring manual intervention. The transformation process is self-executing, taking raw probability outputs and automatically generating intuitively meaningful values close to 0 or 1, thereby eliminating labor-intensive manual categorization while maintaining user understanding.
3Ease of operation
If custom algorithms are developed for each domain, then intuitive predicted probabilities can be generated, but the investment required is expensive and difficult
Solution Approach 1:
The patent applies a universal mathematical transformation function that can be used across different domains and machine learning models. This single transformation approach works generally for binary classification problems regardless of the specific domain, eliminating the need to develop custom algorithms for each use case while maintaining intuitiveness.
4Ease of operation
If probability transformation is applied to move predictions closer to 0 or 1, then intuitive probabilities are achieved, but the distance from center probabilities increases
Solution Approach 1:
The transformation function applies asymmetric mapping that treats probabilities differently based on their position. Probabilities above 0.5 are transformed to be closer to 1, while probabilities below 0.5 are transformed to be closer to 0. This asymmetric transformation creates the desired intuitive distribution while maintaining the separation between success and failure cases.
Data Source
AI summary
Systems and methods for training a machine learning (ML) model for predicting probabilities for binary outcomes to automatically generate positive class predictions close to an ideal one probability and negative class predictions close to an ideal zero probability are disclosed. The method includes generating a predictive probability (PP) curve based on a ML algorithm and transforming the PP curve into a curve with probabilities spread close to ideal one probability for positive class predictions (PCP) indicating successful prediction and close to ideal zero probability for negative class predictions (NCP) indicating failed prediction, thereby introducing a valley in the transformed probability curve separating PCP from NCP. The PP curve is transformed by one of (1) minimizing distance between: (a) ideal one probability and PP value of PCP; and (b) ideal zero probability and PP value of NCP, and (2) maximizing distance of PP values from center of PP curve.