You work as a machine learning specialist for a computer hardware component producer. Your company produces individual components, such as processor chips, GPUs, etc. and assembled computer peripherals such as monitors, external disk drives, among others. You and your team have been tasked with building a machine learning model that predicts future product sales to improve supply chain management based on data from your semiconductor, transistor, and other base component suppliers as well as data from your sales department. After training your model, you now need to evaluate it to determine whether its performance and accuracy will allow you to use it to predict future product sales accurately. You have decided to perform an offline model evaluation of your model using your historical data. You have split your validation dataset into 10 parts. You then execute 10 training runs, which produces 10 models. You then aggregate the 10 models to get your final evaluation. Which model evaluation method are you using?

Answer options:

A.Holdout set validation
B.K-fold cross-validation
C.Bayesian validation
D.Hierarchical validation

Correct Answer: B
Option A is incorrect. Holdout validation uses one validation dataset to use in model validation. Using this method, you evaluate your model using this one holdout set. But you have split your validation dataset into 10 parts. So you will have 10 training runs in your validation process.
Option B is correct. You are using the k-fold cross-validation technique. Using this validation method, you split the example dataset into k parts, in your case, k = 10. You then run 10 training using each of the 10 example datasets. Finally, using this method, you aggregate the 10 run results to get your final evaluation.
Option C is incorrect. There is no bayesian validation method.
Option D is incorrect. There is no hierarchical validation method.
References:
Please see the Amazon SageMaker developer guide titled Validate a Machine Learning Model (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-model-validation.html),
The Towards Data Science article titled Validating your Machine Learning Model (https://towardsdatascience.com/validating-your-machine-learning-model-25b4c8643fb7)