Interactive Machine Learning Model Development Framework
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Solution Overview
Problem
Machine learning models are often considered 'black boxes,' making it difficult for subject matter experts to understand how they arrive at results, which reduces confidence in their use and reliability.
Innovation Solution
An interactive framework for machine learning model development that incorporates domain knowledge through a graphical user interface, allowing experts to visualize and refine models in real-time, creating a 'white box' or human-centric approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning algorithms are used, then predictive accuracy can be achieved, but the model becomes a black box that is difficult to interpret
Solution Approach 1:
The patent introduces subject matter expert feedback as an intermediary between the machine learning model and the final decision-making process. The feedback mechanism allows experts to provide domain knowledge that guides feature selection and model interpretation, making the black box model's decisions transparent and explainable while maintaining predictive accuracy
2Extent of automation
If machine learning models are developed without human interaction, then automation is improved, but domain knowledge cannot be incorporated
Solution Approach 1:
The patent implements a dynamic model development process where the level of automation adjusts based on user needs. The system can operate in highly automated modes for rapid prototyping or transition to interactive modes where subject matter experts actively participate in feature selection and model refinement, allowing flexibility between automation and human expertise
3Loss of information
If interactive model building is implemented, then model interpretability is improved, but development complexity increases
Solution Approach 1:
The patent implements self-service capabilities where the system automatically performs routine tasks such as data preprocessing, feature engineering suggestions, and model training based on expert feedback. This automation of administrative tasks reduces the overall complexity burden on subject matter experts while maintaining interactive interpretability features
Data Source
AI summary
A method is provided that includes generating a visual environment for interactive development of a machine learning (ML) model. The method includes accessing observations of data each of which includes values of independent variables and a dependent variable. The method includes performing a feature construction and selection based on the interactive EDA, and in which select independent variables are selected as or transformed into a set of features for use in building a ML model to predict the dependent variable. The method includes an interactive model building to build the ML model using a ML algorithm, the set of features, and a training set produced from the set of features and observations of the data. And the method includes outputting the ML model for deployment to predict the dependent variable for additional observations of the data.