Automated ML Model Refinement With Explainable Feature Analysis
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Solution Overview
Problem
Machine learning models are laborious to develop, lack transparency, and suffer from algorithmic bias, making them difficult to understand and diagnose, especially when used for critical decision-making.
Innovation Solution
An automated system generates customized machine learning models through a user interface, asking task-specific questions to gather data and analyze models, providing explainable outputs without requiring expert data science skills, and refines models using ensemble techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to solve complex problems, then problem-solving capability is improved, but transparency and understandability deteriorate
Solution Approach 1:
The patent introduces an explanation generation module that acts as an intermediary between the machine learning model and the user. This module translates the model's internal decision-making processes into human-understandable explanations, allowing users to comprehend why specific predictions were made without simplifying the underlying complex algorithms. The explanation module bridges the gap between black-box model operations and user understanding.
2Productivity
If automated systems are used to build machine learning models, then development speed is improved, but model complexity increases
Solution Approach 1:
The automated model building system is divided into distinct modular components: data preprocessing module, feature engineering module, model selection module, training module, and explanation generation module. Each module handles a specific aspect of the model development process independently, allowing the system to automate complex workflows while maintaining manageable complexity through clear separation of concerns and standardized interfaces between modules.
3Power
If deep models with multiple layers are used, then processing capability is improved, but diagnostic difficulty increases
Solution Approach 1:
The patent implements a feedback mechanism where the explanation generation module continuously provides insights about model behavior back to the user interface. This allows users to observe how different input features contribute to predictions at various layers of the deep model, enabling diagnostic analysis of complex multi-layer architectures by visualizing feature importance and decision pathways without requiring deep expertise in model internals.
Data Source
AI summary
This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.