Tools

LEADER 03286cam a2200517Ii 4500

001 on1231501404;

003 OCoLC;

005 20210802095114.0;

006 m o d ;

007 cr unu||||||||;

008 210114s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1231501404;

035 a| (OCoLC)1231501404;

049 a| EREE;

090 a| Q325.5;

100 1 a| Vilkomir, Aleksei,e| author.?| UNAUTHORIZED;

245 13 a| An empirical exploration of Python machine learning API usage /c| by Aleksei Vilkomir.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 56 pages :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 285.8 KB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2020.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Mark Hills;

500 a| Title from PDF t.p. (viewed July 22, 2021).;

520 3 a| Machine learning is becoming an increasingly important part of many domains, both inside and outside of computer science. With this has come an increase in developers learning to write machine learning applications in languages like Python, using application programming interfaces (APIs) such as pandas and scikit-learn. However, given the complexity of these APIs, they can be challenging to learn, especially for new programmers. To create better tools for assisting developers with machine learning APIs, we need to understand how these APIs are currently used. In this thesis, we present a study of machine learning API usage in Python code in a corpus of machine learning projects hosted on Kaggle, a machine learning education and competition community site. We analyzed the most frequently used machine learning related libraries and the sub-modules of those libraries. Next, we studied the usage of different calls used by the developers to solve machine learning tasks. We also found information about which libraries are used in combination and discovered a number of cases where the libraries were imported but never used. We end by discussing potential next steps for further research and developments based on our work results.;

504 a| Includes bibliographical references.;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Application program interfaces (Computer software)=| ^A425973;

650 0 a| Python (Computer program language)=| ^A809520;

653 a| Machine Learning API;

653 a| Machine Learning exploratory;

700 1 a| Hills, Mark,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/8796;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5547864;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547864-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/14/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547864-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/14/2021x| ETDz| HERESOURCE;

An empirical exploration of Python machine learning API usage / by Aleksei Vilkomir.

Author/creator	Vilkomir, Aleksei author.
Other author	Hills, Mark, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	56 pages : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Machine learning. Application program interfaces (Computer software) Python (Computer program language)

Summary	Machine learning is becoming an increasingly important part of many domains, both inside and outside of computer science. With this has come an increase in developers learning to write machine learning applications in languages like Python, using application programming interfaces (APIs) such as pandas and scikit-learn. However, given the complexity of these APIs, they can be challenging to learn, especially for new programmers. To create better tools for assisting developers with machine learning APIs, we need to understand how these APIs are currently used. In this thesis, we present a study of machine learning API usage in Python code in a corpus of machine learning projects hosted on Kaggle, a machine learning education and competition community site. We analyzed the most frequently used machine learning related libraries and the sub-modules of those libraries. Next, we studied the usage of different calls used by the developers to solve machine learning tasks. We also found information about which libraries are used in combination and discovered a number of cases where the libraries were imported but never used. We end by discussing potential next steps for further research and developments based on our work results.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Mark Hills
General note	Title from PDF t.p. (viewed July 22, 2021).
Dissertation note	M.S. East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

An empirical exploration of Python machine learning API usage / by Aleksei Vilkomir.

Click here for more information about this title

Availability