Microsoft Azure AI Fundamentals — Question 9

For a machine learning progress, how should you split data for training and evaluation?

Answer options

• A. Use features for training and labels for evaluation.
• B. Randomly split the data into rows for training and rows for evaluation.
• C. Use labels for training and features for evaluation.
• D. Randomly split the data into columns for training and columns for evaluation.

Correct answer: B

Explanation

The correct answer is B because randomly splitting the data into rows ensures that both training and evaluation datasets are representative of the overall dataset, allowing for effective model training and validation. Options A and C incorrectly suggest using features and labels inappropriately for training and evaluation, while D suggests an incorrect approach of splitting columns which does not typically provide a valid training and testing method.