In order to estimate predictive power of the model, we:
1)split the data on the training set(80%) and test set(20%).
2)Then we build the model based on training set
3)We use, for example, k-fold cross-validation in order to determine how good our model is.
Question 1 : I cannot understand why do we need to do first step, when in the third step(k-fold cross-validation) does the same procedure even better?
Question 2: Do we need to do something after third step? Our it is the last step of procedure? If so, why do we need to do first step?