You work as a machine learning specialist for a clothing manufacturer. You have built a linear regression model using SageMaker’s built-in linear learner algorithm to predict sales for a given year. Your training dataset observations are based on several features such as marketing dollars spent, number of active stores, traffic per store, online traffic to the company website, overall market indicators, etc. You have decided to use the k-fold method of cross-validation to assess how the results of your model will generalize beyond your training data.
Which of these will indicate that you don’t have biased training data?

Answer options:

A.The variance of the estimate increases as you increase k.
B.You shouldn’t have to worry about bias because your error function removes bias in the data.
C.Every k-fold cross-validation round increases the training error rate.
D.Every k-fold cross-validation round has a very similar error rate to the rate of all the other rounds.
E.You would not normally use k-fold with linear regression models.

Answer: D
Option A is incorrect. When using k-fold for cross-validation, the variance of the estimate is reduced as you increase k. So 10-fold cross-validation should have a lower variance than 5-fold cross-validation.
Option B is incorrect. The k-fold error function just gives you the error rate of the cross-validation round. It doesn’t resolve bias.
Option C is incorrect. The goal of k-fold cross-validation is to produce relatively equal error rates for each round (indicating proper randomization of the data), not to reduce the error rate for each round.
Option D is correct. If you have relatively equal error rates for all k-fold rounds, it is an indication that you have properly randomized your test data, therefore reducing the chance of bias.
Option E is incorrect.The k-fold cross-validation technique is commonly used with linear regression analysis.
Reference:
Please see the Amazon Machine Learning developer guide titled Evaluating ML Models, and the Amazon Machine Learning developer guide titled Cross-Validation