A Study of Model Selection for Electric Data using Cross Validation Approach

Saraswathi Sivamani; Saravana Kumar; Chang-Sun Shin (신창선); Park Jang Woo (박장우); Cho Yong Yun (조용윤)

doi:10.34163/jkits.2017.12.6.005

A Study of Model Selection for Electric Data using Cross Validation Approach

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2017, 12(6), pp.837-844
DOI : 10.34163/jkits.2017.12.6.005
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Published : December 31, 2017

Saraswathi Sivamani ¹, Saravana Kumar ¹, Chang-Sun Shin ¹, Park Jang Woo ¹, Cho Yong Yun ¹

¹순천대학교

Accredited

ABSTRACT

In this paper, the appropriate model is selected for the risk assessment of the electric utility pole data with the help of cheat sheets and k-fold cross validation. In order to analyze, predict and forecast the data, the appropriate model has to be selected. The major issue is the declination of the accuracy in the model fitting, which may result in poor model selection. There are different type of machine learning algorithm, which makes it difficult to conclude the model selection. To ensure the proper selection of the model, we undergo a two-step process. Firstly, the basic model is selected with the existing model selection cheat sheets named as Scikit learn and Microsoft azure, by understanding the available input and required output of the data. After getting through the multiple question, the respective models such as Generalized Additive Model, Generalized Linear Model, Linear Regression and Support Vector Machine are obtained. In order to attain the appropriate model, we perform k-fold cross validation to estimate the risk of the algorithms, by comparing 2-fold, 8-fold and 10-fold cross validation. Between the three set, the 10-cross fold validation of generalized additive model is selected with the least risk error. Using k-fold cross validation, we estimate the accuracy of the model that is suitable for the data, by using the electric power data set.

KEYWORDS

Model selection, K-fold cross validation, Machine learning, Model fit, Electric power

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* References for papers published after 2023 are currently being built.

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study of Model Selection for Electric Data using Cross Validation Approach

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* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study of Model Selection for Electric Data using Cross Validation Approach

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* References for papers published after 2023 are currently being built.