Machine Learning Made Easy: A Review of 'Scikit-learn' Package in Python Programming Language
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| Title: | Machine Learning Made Easy: A Review of 'Scikit-learn' Package in Python Programming Language |
|---|---|
| Language: | English |
| Authors: | Hao, Jiangang, Ho, Tin Kam |
| Source: | Journal of Educational and Behavioral Statistics. Jun 2019 44(3):348-361. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2019 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Artificial Intelligence, Statistical Inference, Data Analysis, Programming Languages, Open Source Technology, Computer Software |
| DOI: | 10.3102/1076998619832248 |
| ISSN: | 1076-9986 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2019 |
| Accession Number: | EJ1214827 |
| Database: | ERIC |
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