Monotonic Statistical Model Predictions Under Variable Changes
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Solution Overview
Problem
Conventional statistical models often produce counterintuitive or inaccurate predictions when independent variable values are altered, leading to unexpected changes in dependent variable values.
Innovation Solution
A software and/or hardware facility that enforces monotonicity in predictions by using a delta model approach or innate monotonicity approach, ensuring that dependent variable values change in the expected direction with changes in independent variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional statistical models are used for prediction, then the model can be trained using standard methods, but the predictions may be counterintuitive or inaccurate when independent variable values are altered
Solution Approach 1:
The patent applies monotonicity constraints during the model training process before the model is deployed for predictions. By pre-establishing these constraints on the relationship between independent and dependent variables, the model is guided to learn patterns that are both statistically accurate and intuitively consistent, preventing counterintuitive predictions from the outset
Solution Approach 2:
The patent modifies the training approach by incorporating monotonicity constraints as additional parameters or conditions during model training. This changes the optimization landscape to favor models that not only fit the training data well but also maintain consistent monotonic relationships, thereby improving prediction reliability without sacrificing accuracy
2Reliability
If monotonicity constraints are enforced in the model, then prediction reliability improves, but model complexity increases
Solution Approach 1:
The patent applies monotonicity constraints selectively to specific distinguished independent variables rather than enforcing them uniformly across all variables. This localized application of constraints reduces the overall complexity burden while still ensuring prediction reliability for the critical variables where monotonicity is most important
Solution Approach 2:
The patent implements monotonicity constraints as a partial solution during training, applying them to key variables rather than all variables. This partial enforcement achieves the necessary reliability improvement without the full complexity cost of universal monotonicity enforcement, representing a pragmatic balance between constraint application and model simplicity
Data Source
AI summary
A facility for estimating a value relating to a occurrence is described. The facility receives a first occurrence that specifies a first value for each of a plurality of independent variables that include a distinguished independent variable designated to be monotonically linked to a dependent variable. The facility subjects the first independent variable values specified by the received occurrence to a statistical model to obtain a first value of the dependent variable. The facility receives a second occurrence that specifies a second value for each of the plurality of independent variables, the second value of the distinguished independent variable varying from the first value of the distinguished independent variable in a first direction. The facility subjects the second independent variable values specified by the received occurrence to the statistical model to obtain a second value of the dependent variable, the second value of the dependent variable being guaranteed not to vary from the first value of the dependent variable in a second direction that is opposite the first direction.