9. If we are implementing 10-Fold Cross Validation on 100 observations, then

Answers:

-First ten rows in the data frame make fold 1, next ten rows make fold 2, and so on. There are 10 iterations. For each iteration, there are 90 observations in the training set and 10 in the validation set.

- The data are randomly assigned to one of ten folds. There are 10 iterations. For each iteration, there are 90 observations in the training set and 10 in the validation set.

-First ten rows in the data frame make fold 1, next ten rows make fold 2, and so on. There are 10 iterations. For each iteration, there are 10 observations in the training set and 90 in the validation set.

- The data are randomly assigned to one of ten folds. There are 10 iterations. For each iteration, there are 10 observations in the training set and 90 in the validation set.

- Randomly split 10 observations into the validation data set and perform a single run on the 90 training data to predict the 10 validation observations.

10. As we make more complex the model, RMSE computed from the validation (out-of-sample) data set can potentially increase.

True or False