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

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

View Answer

Answer: D

Explanation:

In Azure Machine Learning, the percentage split is the available technique to split the data.

In this technique, random data of a given percentage will be split to train and test data.

Reference: https://www.sqlshack.com/prediction-in-azure-machine-learning/