How to run a prediction model on a dataset without spliting it to train and test datasets

mansour

Hello everyone,

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.

Thank you,

Mansour

RolandJones

Hi Mansour,

I would look to use cross-validation instead of using a train/test split. This is a more advanced, iterative technique, that uses folds in the data to both train and test on the entire dataset.

I might suggest starting with 5 folds, based off the size you said, and improve from there. Here’s a good blog on cross validation where you can get a little more information: https://rapidminer.com/blog/validate-models-cross-validation/. I believe we also have a video on the Academy.

Best,
Roland