So as far as i know, there are many ways to evaluate a M.L/D.L model. Some of them, for the sake of the example, are k-fold cross validation and test/validation/train splits.
Now with regards to validation sets. Say if you have a dataset of 100 rows and 3 columns, 2 of which [columns] are independent features & the last one [3rd column] is the dependent variable vector and wanted to split dataset into training, validation and test sets, would the validation set be a part of the training set? [seperate from the test set?]
In other words, it is most common, with small datasets, to use the 80/20 split; where 80% of the data goes to training and 20% goes to testing. My question, is the validation set within the 80% such that:
(validation + training set) + test set = 100% of the data?
i.e. (20% + 60%) + 20% = 100%
Or am I getting this all mixed up and the validation set is in fact the test set but for a model that is still having its parameters tunes?
P.S I apologize for what seems to be a mess of a question, but this in fact reflects the state in which my mind seems to be in with this topic.