Predictive Analytics Model Framework for Automated ML Selection
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Solution Overview
Problem
Existing methods for developing Predictive Analytics models using Machine Learning Algorithms are manual and error-prone, requiring significant effort and specialized knowledge, and lack intuitive user interfaces for non-technical users.
Innovation Solution
A Framework that provides an intuitive user interface and automated processes for selecting, training, and deploying machine learning algorithms, utilizing ensemble methods and real-time predictive alerts through single board computers or display monitors, reducing manual effort and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional manual methods are used to select and train machine learning algorithms, then model development can be performed with basic tools, but significant manual effort and specialized knowledge are required
Solution Approach 1:
The system performs automated algorithm selection and model training without requiring manual intervention. The framework automatically evaluates multiple algorithms, selects the best performing ones, and trains ensemble models based on the input data characteristics, eliminating the need for manual trial-and-error experimentation
Solution Approach 2:
The framework pre-evaluates and ranks multiple machine learning algorithms before the user needs to select one. By performing algorithm evaluation and selection in advance based on data characteristics, the system prepares ready-to-use model recommendations that save significant time during actual model development
2Adaptability or versatility
If traditional manual methods are used for model development, then fewer computational resources are needed, but the process requires advanced technical knowledge and is not intuitive for business users
Solution Approach 1:
The framework acts as an intermediary layer between business users and complex machine learning algorithms. It provides a simplified interface that translates business requirements into appropriate algorithm selections and model configurations, shielding users from technical complexity while maintaining access to advanced capabilities
Solution Approach 2:
The system segments the complex model development process into distinct, manageable steps including data exploration, algorithm selection, model training, and evaluation. Each step is presented as a separate interface element that guides users through the process sequentially, making the overall complex task more approachable
3Reliability
If single algorithm models are used, then the development process is simpler, but accuracy is reduced compared to ensemble methods
Solution Approach 1:
The framework merges multiple machine learning algorithms into ensemble models that combine their predictive capabilities. By evaluating and selecting complementary algorithms and training them together on the same data, the system achieves higher prediction accuracy than any single algorithm could provide alone
Solution Approach 2:
The system changes the parameter of algorithm diversity by selecting and combining multiple different algorithms rather than using a single algorithm. This parameter change from single to multiple algorithms is what enables the accuracy improvement while the automated framework manages the resulting complexity
Data Source
AI summary
A Framework for developing, training and deploying Predictive Analytics Models using Artificial Intelligence based Machine Learning algorithms is presented. The Framework is a collection of processes with User Interface and Data Processing components which interact with input data acquisition, output data visualization and data transmission systems. The key part of the Framework is automation of processes involved in building, training and deploying a model, with the aim of reducing the time and manual effort normally required in such tasks. The Framework also includes a proprietary method of machine learning algorithm selection process based on use case and certain characteristics of data, that ultimately results in improved accuracy of Predictive Analytics models for real time and non-real time applications.