Outlier Prediction Using Random Forest Classifier
Divya Pramasani Mohandoss, Yong Shi, Kun Suo
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.