How to run a prediction model on a dataset without spliting it to train and test datasets
I hope this message finds you well. I am currently working on a project that involves running RapidMiner prediction models on a dataset. Specifically, I am interested in using tree induction, SVM, DM, and other models to predict outcomes and determine prediction accuracy.
However, I am faced with a challenge in that my dataset only contains 60 samples, which makes it difficult to split it into training and testing datasets. Therefore, I am reaching out to you to see if anyone has any suggestions on how I can proceed with running the models without having to split the dataset.
I greatly appreciate any insights or advice you may have on this matter.