Automated Model Development Tool for Analytical Accuracy
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Solution Overview
Problem
Manually developing accurate analytical models is challenging due to the complexity of calibrating algorithms and selecting appropriate data sets, which can lead to errors and decreased accuracy in pattern recognition and prediction.
Innovation Solution
An automated model development tool that analyzes predictor variables, combines similar data, reduces the number of predictor variables based on predictive strength, and develops analytical models to identify relationships between predictor variables and output variables, using techniques like genetic algorithms and automatic binning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to develop analytical models, then flexibility and control over model development are maintained, but accuracy and efficiency deteriorate due to complexity in calibrating algorithms and selecting data sets
Solution Approach 1:
The system performs automated model development where the computational system itself carries out data exploration, variable selection, algorithm calibration, and model development without requiring manual intervention for each step, thereby improving accuracy while reducing the practical complexity burden on users
Solution Approach 2:
The system automatically adjusts and calibrates algorithm parameters through computational methods, transforming the manual parameter tuning process into an automated optimization process that improves model accuracy without increasing user-facing complexity
2Productivity
If manual methods are used to develop analytical models, then control over each development step is maintained, but productivity deteriorates due to time-consuming data formatting and manipulation
Solution Approach 1:
The system performs preliminary automated actions including data exploration, variable selection, and data formatting before the actual model development process, eliminating the need for manual data preparation and significantly improving productivity
Solution Approach 2:
The computational system automatically handles data formatting, manipulation, and preparation tasks that would otherwise require manual time investment, enabling faster model development while maintaining data quality
3Productivity
If automated model development is implemented, then productivity and accuracy improve, but device complexity increases due to multiple processing steps and algorithms
Solution Approach 1:
The system integrates multiple functions including data exploration, variable selection, algorithm calibration, and model development into a single automated platform, managing internal complexity while presenting a unified interface that maintains productivity benefits
4Productivity
If automated model development is implemented, then efficiency improves, but ease of operation deteriorates due to reduced manual control and increased automation
Solution Approach 1:
The system automates complex development steps that would be difficult to control manually, improving efficiency by handling intricate algorithm calibration and variable selection automatically while reducing the operational burden on users
Data Source
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AI summary
An automated model development tool can be used for automatically developing a model (e.g., an analytical model). The automated model development tool can perform various automated operations for automatically developing the model including, for example, performing automated operations on variables in a data set that can be used to develop the model. The automated operations can include automatically analyzing the predictor variables. The automated operations can also include automatically binning (e.g., combining) data associated with the predictor variables to provide monotonicity between the predictor variables and one or more output variables. The automated operations can further include automatically reducing the number of predictor variables in the data set and using the reduced number of predictor variables to develop the analytical model. The model developed using the automated model development tool can be used to identify relationships between predictor variables and one or more output variables in various machine learning applications.