1. Identify a difference between the model evaluation methods of hold-out variation and N-fold cross validation.

Multiple Choice

• Unlike the N-fold cross validation method, the hold-out variation method divides data into many smaller subsets.

• Unlike in the hold-out variation method, the N-fold cross validation method randomly selects one set of two-thirds of data to build the regression model.

• Unlike the hold-out sample method, the N-fold cross validation method is less sensitive to variation in training and validation datasets selection.

• Unlike the N-fold cross validation method, the hold-out variation method requires high amounts of training time and computer processing power.

2. When high levels of accuracy in a training dataset do not apply to predicting models using new data, the phenomenon is termed ________.

Multiple Choice

• multicollinearity

• overfitting

• adjacency

• dummy coding