Open AccessGraphical user interface (GUI) for the least absolute shrinkage and selection operator (LASSO) regression
Author(s) -
Gerry Alfa Dito,
Ani Safitri,
Farit Mochamad Afendi,
Rahma Anisa,
Agus Salim,
Bagus Sartono
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/299/1/012031
Subject(s) - lasso (programming language) , covariate , elastic net regularization , computer science , selection (genetic algorithm) , regression , regression analysis , feature selection , operator (biology) , data mining , linear regression , machine learning , artificial intelligence , statistics , mathematics , programming language , biochemistry , chemistry , repressor , transcription factor , gene
In Data Science, we usually encounter High-dimensional data. In this situation, the Classical Regression method usually cannot perform well because it is impossible to include all covariates in the model since the number of a parameter to be estimated is larger than the sample size. Least absolute shrinkage and selection operator (Lasso) method is one of the methods which can deal with this problem. Lasso regression perform the selection of covariates so that only the most influential covariates are used in the model. Unfortunately, most of Lasso method should be performed in CLI Software which is difficult to use for the general user. For this reason, we develop a web application by using Shiny to perform the Lasso method based on GUI which is easier to use. It allows users to analyze high-dimensional data without using programming language. The paper contains an implementation of Lasso Regression using web application on olive pomade oil data.