Proper way to split dataset (split ratio) and evaluate the baseline model or fitted model in the firsthand
I appreciate your time in this matter. I really need an answer for this, for my thesis. I use the PaySim dataset from Kaggle ( https://www.kaggle.com/datasets/ealaxi/paysim1 ). First of all, I use training, validation, and testing set. Is there really a rule on data split ratio and is it acceptable if I check the model performance on each split, for example, 70/15/15, 80/10/10? After fitting the model with training dataset, we get the default model/fitted model. Which dataset (Training or Validation set) shall I use for examining the model performance? My intention of having 3 types of set (Training, Validation, and Testing) is to use the Validation set as hyperparameter tuning examination. Thanks so much.