Why is it important to create data splits that are reproducible?

• A.To use more data for testing than for training
• B.To allow for larger test sets for more comprehensive testing
• C.To guarantee that the model will perform with 100% accuracy on unseen data
• D.To ensure that each model run can be exactly replicated for verification and comparison

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In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?

• A.It guarantees that the model will perform with 100% accuracy on unseen data
• B.It ensures that the model is trained on the maximum amount of data possible
• C.It increases the computational complexity without improving model performance
• D.It allows for the model to be validated and tested on different subsets of data to check its generalization ability

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An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?

• A.Business Understanding
• B.Data Understanding
• C.Data Preparation
• D.Modeling

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What is a key advantage of using supervised learning techniques over unsupervised learning techniques?

• A.Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
• B.Supervised learning can work without any labeled data.
• C.Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
• D.Supervised learning algorithms can automatically label data.

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