I have data of a class of 50 students. My dataset consists of historical data of students in previous years. Data includes presence, homework grades and final grade of previous 10 years (X, training dataset, has 500 elements).
I have a model trained with this data to predict the final grade.
What would be more accurate way to validate my model? Training it with data a range of years (lets say 2005, 2010) and measuring the error of prediction of 2011 data. Or separating a random sample of the training data and estimating the prediction error of this validation sample?