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Overview of Machine Learning Process Modelling.

Boštjan Brumen1, Aleš Černezel1, Leon Bošnjak1

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia.

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Summary
This summary is machine-generated.

The power law model best describes machine learning algorithm performance curves, accurately predicting future capabilities. This research offers a robust method for assessing artificial learner performance across various datasets.

Keywords:
data mininglearning curveslearning processmachine learningpower law

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • General theories describing artificial learner performance are lacking.
  • Empirical performance assessment of machine learning algorithms remains an open research question.

Purpose of the Study:

  • To identify the most appropriate function for describing machine learning algorithm learning curves.
  • To evaluate the predictive power of these learning curves for future algorithm performance.

Main Methods:

  • Applied four machine learning algorithms (Decision Trees, Neural Networks, Naïve Bayes, Support Vector Machines) to 130 datasets.
  • Fit three functions (power, logarithmic, exponential) to the empirical learning curves.
  • Utilized statistical methods and goodness-of-fit measures to compare models.

Main Results:

  • The power law model demonstrated the best goodness-of-fit and predictive capabilities for learning curves.
  • This model effectively described the performance of the tested machine learning algorithms.

Conclusions:

  • The power law provides a generalizable model for artificial learner performance.
  • This framework enables assessment and forecasting of machine learning algorithm capacity based on data availability.