Interactive Text Regression Model Building Interface
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Solution Overview
Problem
Current regression models for text analysis are inefficient in updating and comparing models, particularly in identifying key textual terms that contribute to predictive outcomes, due to manual and error-prone user interactions and lack of streamlined interfaces for model building and interpretation.
Innovation Solution
A computer-program product and method for generating an updated model for regression models, where a computing system receives textual terms, user selections, and target values to estimate relationships, and displays model performance and term contributions, allowing users to interactively adjust and compare models through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user interactions are used for model building and updating, then users can control the model generation process, but the process becomes error-prone and inefficient
Solution Approach 1:
The system enables users to interactively build and update regression models through a graphical interface where the computing system automatically performs model generation, performance evaluation, and term contribution analysis. The user selects textual terms and target values, while the system handles the complex computational tasks of model fitting and comparison, reducing manual errors and improving efficiency.
2Loss of information
If comprehensive model analysis and comparison features are provided, then users can make informed decisions about model selection, but the interface complexity increases
Solution Approach 1:
The graphical user interface presents model information in segmented, organized sections: model performance metrics, term contribution analyses, and comparison tools are separated into distinct interactive elements. This allows comprehensive model analysis to be delivered through a structured, manageable interface that prevents information overload while maintaining completeness.
3Measurement precision
If interactive model adjustment capabilities are provided, then users can optimize model performance, but the time required for model building increases
Solution Approach 1:
The system performs preliminary model generation and performance evaluation automatically when initial parameters are set. Users can then make targeted adjustments based on pre-computed term contributions and performance metrics, rather than building models from scratch. This preliminary analysis accelerates the iterative optimization process while maintaining precision.
Data Source
AI summary
A computing system receives as candidate predictors, for a model set, a list of terms for computer identification in dataset(s). The system receives initial user selections in the graphical user interface (GUI) of a term set, a response variable, and target value(s) for the response variable. The term set comprises candidate predictors from the list. The response variable is for a response to input to an initial model of the model set. The system generates the initial model that estimates a relationship between the target value(s) and the term set. The system displays in the GUI a performance representation of the initial model for user comparison of models and an indication of a contribution, to the initial model, of terms of a subset of the term set. The system receives a subsequent user selection, in the GUI, to change an aspect of the initial model.