Given some dataset for prediction, for eg say I have different housing price prediction dataset:dataset  1 : 100 training and 100 testing sample, 50 featuredataset  2 : 100 training and 100 testing sample, 120 feature dataset  3 : 1000 training and 1000 testing sample, 50 featuredataset  4 : 1000 training and 1000 testing sample, 5000 featurehow should I choose the best methods for estimating the unknown parameters ( predict price) in a linear regression model from the following for each of these dataset?Ordinary least squaresStepwise regressionPrincipal component regression Partial least squares regressionShould I experiment with each of these one by one and compare the results or is there any rule of thump on when to use each of them based on the dataset ? Please help
Choosing best methods for estimating the unknown parameters in a linear regression model
machine learning;regression