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Progress Indication for Machine Learning Model Building: A Feasibility Demonstration.

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Summary
This summary is machine-generated.

This study demonstrates the feasibility of progress indicators for machine learning model building. Detailed implementation techniques are provided for supervised learning algorithms, showing progress estimation is viable.

Keywords:
Machine learningWekaprogress indicator

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Area of Science:

  • Computer Science
  • Machine Learning

Background:

  • Machine learning model building is time-consuming.
  • Progress indicators are needed to estimate remaining time and completion.
  • A prior framework lacked detailed implementation.

Purpose of the Study:

  • To demonstrate the viability of progress indicators for machine learning.
  • To provide detailed implementation techniques for progress indicators.

Main Methods:

  • Developed implementation techniques for progress indicators.
  • Focused on three major supervised machine learning algorithms.
  • Implemented techniques within the Weka software.

Main Results:

  • Successfully demonstrated the feasibility of progress indicators.
  • Provided concrete implementation details for supervised learning.
  • Showcased practical application in Weka.

Conclusions:

  • Progress indicators for machine learning model building are feasible.
  • Detailed techniques enable practical implementation.
  • This work bridges the gap between framework and application.