One of the most common mistakes data scientists make when training machine learning models is incorrectly splitting data for training and testing. The train/test split involves splitting data during the model training and evaluation process.
Learner makes this simple with a single parameter selection during the model building process. It’s also simple to set the percentage split between training and testing data for each model trained.