Data Flow-Based Strategies to Improve the Interpretation and Understanding of Machine Learning Models

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Artificial intelligence (AI) models, such as artificial neural networks and random forest models, are complex.
• Understanding the internal workings and data interpretation of these AI models is challenging.
• Ensuring the stability and reliability of AI model results across different data sources is crucial.

Purpose of the Study:

• To examine data flow-based strategies for improving the interpretability of AI model results.
• To investigate how data curation and monitoring impact AI model understanding.
• To enhance the stability of AI-based supervised modeling across various data sources.

Main Methods:

• Careful curation and monitoring of data flow into AI models, including artificial neural networks and random forest models.
• Analyzing how restricted data inputs provide insights into model interpretation of data structures and variables.
• Assessing the impact of data variation on AI model performance and stability.

Main Results:

• Data flow strategies offer valuable insights into how AI models interpret data.
• Restricting data inputs can reveal model sensitivities to data structures and variations.
• Understanding data flow aids in improving the stability of AI models across datasets.

Conclusions:

• Data flow-based strategies are effective for enhancing the interpretability of AI models.
• Careful data management is key to understanding and stabilizing AI model performance.
• Guidelines for initial data adjustments are proposed to address data-related issues in AI modeling.