Predictive Model Generator Using Momentum Analysis
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Solution Overview
Problem
Conventional techniques for generating predictive models are inefficient and inflexible, making it difficult to quickly analyze data and predict various output values across different industries.
Innovation Solution
A system utilizing a hybrid algorithm based on simulated annealing and binary search analyses to iteratively adjust model parameters, employing momentum analysis and momentum multipliers to refine predictive models, allowing for rapid generation of versatile predictive models that can predict multiple output values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used to generate predictive models, then model generation is performed with standard methods, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple model templates with different structures and algorithms before actual prediction is needed. These templates are prepared in advance with configurable parameters, allowing the system to quickly instantiate and customize models without performing full model generation from scratch during prediction tasks.
Solution Approach 2:
The system dynamically selects and configures model parameters based on the specific prediction task requirements. The model generator can adjust model architecture, algorithm selection, and hyperparameters in real-time according to the input data characteristics and desired output, enabling adaptive optimization without retraining entire models.
2Adaptability or versatility
If conventional predictive models are used, then a single model structure is employed, but the system lacks flexibility to predict multiple different output values
Solution Approach 1:
The system implements a universal model generator that can produce multiple types of predictive models (classification, regression, clustering) using a unified framework. The generator accepts different template types and configures them appropriately for various prediction tasks, allowing a single system to handle diverse output requirements without needing separate specialized models for each task type.
Solution Approach 2:
The system achieves versatility by changing model parameters rather than changing the fundamental model structure. By adjusting configurable parameters such as algorithm selection, feature engineering options, and hyperparameters within the same template framework, the system can adapt to different prediction tasks while maintaining a consistent underlying architecture.
3Measurement precision
If traditional model generation methods are used, then models are generated with standard algorithms, but the convergence speed and accuracy are insufficient
Solution Approach 1:
The system incorporates feedback mechanisms where model performance is continuously evaluated during the generation process. The generator uses validation results to iteratively adjust parameters and select optimal configurations, ensuring high accuracy while avoiding unnecessary iterations. This feedback-driven approach prevents both underfitting and excessive over-engineering of models.
Data Source
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AI summary
Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, can be implemented to perform actions including receiving input data defining a predictive model, the predictive model including multiple features. The actions further include weighting the predictive model iteratively for each feature, using actual data including values for each feature for multiple entities within a population, by iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a momentum factor based on whether a model score improves, the model score being calculated based on the actual data. The actions further include calculating a value score for the entity using the weighted predictive model. Implementations of the present disclosure also include computer-implemented methods for determining unplanned hospitalization risk for a patient.