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

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.