Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Software systems often use progress indicators for user-friendliness during long tasks.
• Current machine learning and data mining software lack non-trivial progress indicators for model building and algorithm execution.

Purpose of the Study:

• To address the absence of progress indicators in machine learning and data mining software.
• To explore the challenges and goals of implementing progress indicators for these computationally intensive tasks.

Main Methods:

• Discussion of the problem of providing progress indicators for machine learning and data mining.
• Proposal of an initial framework for implementing progress indicators.
• Description of two advanced potential uses for these indicators.

Main Results:

• Identified the need for progress indicators in machine learning and data mining.
• Presented a framework and potential applications for progress indicators.
• Highlighted areas for future research in this domain.

Conclusions:

• Implementing progress indicators can significantly improve the usability of machine learning and data mining tools.
• Further research is needed to develop and refine these indicators for complex computational processes.
• The proposed framework and advanced uses aim to stimulate innovation in this area.