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Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

Author

Listed:
  • Jiangang Hao

    (Educational Testing Service)

  • Tin Kam Ho

    (IBM Watson)

Abstract

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn , a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

Suggested Citation

  • Jiangang Hao & Tin Kam Ho, 2019. "Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 348-361, June.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:3:p:348-361
    DOI: 10.3102/1076998619832248
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    References listed on IDEAS

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    1. Chen, Aiyou & Bengtsson, Thomas & Ho, Tin Kam, 2009. "A Regression Paradox for Linear Models: Sufficient Conditions and Relation to Simpson’s Paradox," The American Statistician, American Statistical Association, vol. 63(3), pages 218-225.
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    11. Taleh Agasiev & Anatoly Karpenko, 2024. "Exploratory Landscape Validation for Bayesian Optimization Algorithms," Mathematics, MDPI, vol. 12(3), pages 1-21, January.
    12. Ehab Issa El-Sayed & Salah K. ElSayed & Mohammad Alsharef, 2024. "Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques," Sustainability, MDPI, vol. 16(21), pages 1-21, October.
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