Learn how to prepare data and change numeric values to use a common scale in Azure Machine Learning Pipeline Clustering Module. Normalize Data module is used to transform a dataset through normalization, which is a technique often applied as part of data preparation for machine learning.
Table of Contents
Question
You are building an Azure Machine learning pipeline that involves a clustering module. You need to prepare the data and change some of the numeric values from the dataset to use a common scale, without distorting differences in the ranges of values or losing information. Which module should you apply?
A. Edit metadata
B. Normalize Data
C. Split data
Answer
B. Normalize Data
Explanation
The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
To prepare the data and change some of the numeric values from the dataset to use a common scale, without distorting differences in the ranges of values or losing information, you should apply the Normalize Data module. This module is used to transform a dataset through normalization, which is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information. Normalization is also required for some algorithms to model the data correctly.
For example, assume your input dataset contains one column with values ranging from 0 to 1, and another column with values ranging from 10,000 to 100,000. The great difference in the scale of the numbers could cause problems when you attempt to combine the values as features during modeling. Normalization avoids these problems by creating new values that maintain the general distribution and ratios in the source data, while keeping values within a scale applied across all numeric columns used in the model.
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