Description
Split the data randomly into training (75%) and testing (25%), first build the best random forest to predict Spam e-mails using the training data, then use the out-of-bag (OOB) data to measure its performance, and then use this random forest model to predict whether each e-mail in the testing data is Spam or not.
Tools Used
R
Random Forest