Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression

Feature subset selection becomes quite important and predominant in the case of data sets those are contained with higher number of variables. It discards insignificant variables and produces efficient and improved prediction performance on the class variables that is more cost effective and more reliable understanding of the data. Random forest has been emerged as a quite efficient and robust algorithm that can handle feature selection problem even with the higher number of variables. It is also very much efficient while dealing with Missing data imputation, classification, and regression problems. It can also handle outliers and noisy data very well. In this paper we applied the concept of random forest algorithm on the feature subset selection and classification and regression to perform the comparative study of the random forest algorithm in different perspectives.