Outlier Prediction Using Random Forest Classifier

Abstract

Random forest is an ensemble learning method for classification, regression and other tasks that operate by constructing a multiple decision trees using training data and majority of the class will be consider as output. Out-of-Bag (OOB) takes the samples from the training set with replacement. In random forests, if you choose oob to true then there is no need for a separate test set to validate the model. It is estimated internally when the forest is built on training data, and each tree is tested on one-third of the samples not used in building that tree. Out of bag estimate an internal estimate of a random forest as it is being constructed. In this paper we propose two approach to implement outlier prediction by applying random forest classifier and LSTM model with experiments.