I want to run a logistic regression using lasso/ridge regularization for a dataset which has 4500 observations. The number of 1s in the data are 802(18%). I have ~500 predictors of which most of them are dummy (1 or 0). I am unsure how to segment the data for training, validating and testing of my model since the number of data points are very less.
Should I use 80% of the data for training and rest of the data for both validation and testing? Then again the testing results will be biased. If I divide the data into three parts then I am left with inadequate training dataset since number of predictors is large.