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

The correct answer is B. Randomly split the data into rows for training and rows for evaluation.. The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50

Submitted by sofia.br· Mar 30, 2026Describe fundamental principles of machine learning on Azure

Question

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

Options

AUse features for training and labels for evaluation.
BRandomly split the data into rows for training and rows for evaluation.
CUse labels for training and features for evaluation.
DRandomly split the data into columns for training and columns for evaluation.

How the community answered

(47 responses)

A
2% (1)
B
87% (41)
C
2% (1)
D
9% (4)

Explanation

The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

Topics

#ML Data Splitting#Training Data#Evaluation Data#Random Split

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