Firstly, I am new to modelling linear regression models. I want to build a linear regression model to predict building energy use based on building parameters. I'm having a dataset of 100000 buildings with about 40 parameters that have correlation with the building energy use I want to predict. I plan on using a step-wise multiple regression as a starting point of the analysis towards a good regression model.
The literature tells us to always split the dataset into a training and test-set.
- However I found that some people split the dataset in 2 (training and testset) and some people split it in 3 (training, validation and testset). Which approach is better and why?
- What is a good way to divide the data over this training and testset? -> Equally 50-50 or do other other weighted divisions occur (e.g., 80-20)?