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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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    Cited by:

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    3. Ann-Kathrin Edrich & Anil Yildiz & Ribana Roscher & Alexander Bast & Frank Graf & Julia Kowalski, 2024. "A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8953-8982, July.
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    5. Campos, Inês & Korsnes, Marius & Labanca, Nicola & Bertoldi, Paolo, 2024. "Can renewable energy prosumerism cater for sufficiency and inclusion?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    6. Ehsan Harirchian & Tom Lahmer & Shahla Rasulzade, 2020. "Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network," Energies, MDPI, vol. 13(8), pages 1-16, April.
    7. Muhammad Khalid Anser & Munir Ahmad & Muhammad Azhar Khan & Abdelmohsen A. Nassani & Mohamed Haffar & Khalid Zaman, 2024. "The “IMPACT” of Web of Science Coverage and Scientific and Technical Journal Articles on the World’s Income: Scientific Informatics and the Knowledge-Driven Economy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3147-3173, March.
    8. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
    9. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.
    10. Taleh Agasiev & Anatoly Karpenko, 2024. "Exploratory Landscape Validation for Bayesian Optimization Algorithms," Mathematics, MDPI, vol. 12(3), pages 1-21, January.

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