Learn how to standardize numeric features effectively in Azure ML using the Normalize Data module, ensuring consistent scales for improved model training and accuracy.
Table of Contents
Question
You need to bring numeric features to the common scale in your dataset.
What Azure ML Designer module will you use for this purpose?
A. Select Columns in Dataset
B. Clean Missing Data
C. Normalize Data
D. Split Data
E. Clip Values
Answer
C. Normalize Data
Explanation
You’d utilize the “Normalize Data” module in Azure ML Designer to bring numeric features to a common scale. This module scales the values within a specified range, like [0,1] or [-1,1], ensuring all features contribute evenly to model training.
You need to normalize your numeric features. The process of normalization brings numeric features to a common scale.
Azure ML Designer provides the Normalize Data module for this purpose.
Data before the Normalize data module.
And data after Normalize Data module.
Option A is incorrect. “Select Columns in Dataset” module helps select or exclude columns from the model training dataset.
Option B is incorrect. “Clean Missing Data” module takes care of missing data in a dataset.
Option D is incorrect. “Split Data” module divides data into training and testing datasets.
Option E is incorrect. “Clip Values” module detects outliers and clips or replaces their values.
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