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

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...

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Bibliographic Details
Published inJournal of educational and behavioral statistics Vol. 44; no. 3; pp. 348 - 361
Main Authors Hao, Jiangang, Ho, Tin Kam
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publishing 01.06.2019
SAGE Publications
American Educational Research Association
Subjects
Algorithms
Artificial Intelligence
Computer Software
Data Analysis
Learning Modules
Machine learning
Open Source Technology
Programming Languages
Software Review
Statistical Inference
machine learning
Scikit-learn
Python
Online AccessGet full text
ISSN1076-9986
1935-1054
DOI10.3102/1076998619832248

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Summary: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.
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ISSN:1076-9986
1935-1054
DOI:10.3102/1076998619832248