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

Jiangang Hao and Tin Kam Ho
Additional contact information
Jiangang Hao: Educational Testing Service
Tin Kam Ho: IBM Watson

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 3, 348-361

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.

Keywords: machine learning; Python; Scikit-learn (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998619832248 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:3:p:348-361

DOI: 10.3102/1076998619832248

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().