Predictive Model User Interface with Segmented Controls
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Solution Overview
Problem
Current data analysis software lacks an intuitive and efficient method for users to estimate the probability of future events based on user input, with limited interactive features and complex predictive model building processes.
Innovation Solution
A computer system generates a user interface with rating, trend, cohort, record, and source controls that provide data from predictive models for estimating future event probabilities, allowing users to select between basic and enhanced modes, build models, and filter results, with features like search functionality and model performance sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are provided with comprehensive predictive model building capabilities, then the functionality and accuracy of probability estimation is improved, but the interface complexity and difficulty of operation increases
Solution Approach 1:
The interface is segmented into multiple functional tabs (Model Builder, Data Sources, Test Cases, Predictions, Documentation) that organize complex predictive modeling capabilities into discrete, manageable sections. Each tab presents only relevant controls and options for that specific function, reducing cognitive load while maintaining comprehensive functionality.
Solution Approach 2:
The interface dynamically adapts its complexity based on user needs. The Model Builder tab provides advanced options for those who need them, while the basic tab structure remains simple. Users can progressively engage with more complex features as they become familiar with the system, rather than being overwhelmed by all options simultaneously.
2Measurement precision
If multiple interface controls and options are provided for model building, then the precision and accuracy of predictions is improved, but the device complexity increases
Solution Approach 1:
Interface controls are segmented into logical groups within each tab. The Model Builder tab segments model configuration into distinct parameters and options. The Data Sources tab segments data configuration into separate controls. This segmentation allows precision controls to be organized systematically, reducing perceived complexity while maintaining functionality.
Solution Approach 2:
The interface acts as an intermediary that translates complex predictive modeling operations into user-friendly controls. Rather than exposing raw computational parameters, the interface provides standardized controls for model building, data selection, and prediction generation that simplify the interaction while maintaining underlying precision.
3Productivity
If detailed model building features are made available, then the productivity of predictive analysis is improved, but the ease of operation deteriorates
Solution Approach 1:
The interface provides pre-configured options and defaults for common predictive modeling tasks. Users can quickly generate predictions using preset parameters, and only need to modify controls when customization is needed. This preliminary configuration approach enables rapid productivity for standard cases while keeping the full range of detailed controls available when required.
Solution Approach 2:
The interface controls are designed to be universal and multi-functional. The same tab structure and control patterns are used across different predictive modeling tasks, allowing users to develop muscle memory and operational efficiency. This universality enables users to handle diverse predictive analysis needs through consistent interaction patterns, improving productivity without proportionally increasing operational complexity.
Data Source
AI summary
User interfaces for tools for estimating a probability that a future event will occur based on user input are described. One set of interfaces include rating, trend, cohort record and source controls each of which when selected provide corresponding data from one predictive model that produces predictions of the likelihood of an event occurring in the future based on analysis of data in a database. The system further displays a process of content produced by a model builder that populates the interfaces, and outputs thereof.