What is the process of reducing the range of values in numeric variables to a standard range called?

What is the process of reducing the range of values in numeric variables to a standard range called?

• A. Aggregation
• B. Normalization
• C. Discretization
• D. Standardization

Answer: (B)

The process of reducing the range of values in numeric variables to a standard range is known as normalization. Normalization transforms numerical values into a specified range, typically between 0 and 1. This is particularly useful in data preprocessing, especially when working with algorithms that are sensitive to the scale of input features, such as gradient descent-based methods or distance-based algorithms. Normalization ensures that larger values do not dominate the analysis and that each variable contributes equally to the calculation of distances or other metrics. This process is critical in preparing data for machine learning algorithms, enabling better performance and more accurate results. In contrast, other terms like aggregation refer to combining multiple data points into a single summary statistic, while discretization involves converting continuous data into discrete bins or categories. Standardization, on the other hand, typically refers to adjusting data to have a mean of zero and a standard deviation of one, which is slightly different from the aim of normalization. This distinction is essential to understand the specific process being described.

The process of reducing the range of values in numeric variables to a standard range is known as normalization. Normalization transforms numerical values into a specified range, typically between 0 and 1. This is particularly useful in data preprocessing, especially when working with algorithms that are sensitive to the scale of input features, such as gradient descent-based methods or distance-based algorithms.

Normalization ensures that larger values do not dominate the analysis and that each variable contributes equally to the calculation of distances or other metrics. This process is critical in preparing data for machine learning algorithms, enabling better performance and more accurate results.

In contrast, other terms like aggregation refer to combining multiple data points into a single summary statistic, while discretization involves converting continuous data into discrete bins or categories. Standardization, on the other hand, typically refers to adjusting data to have a mean of zero and a standard deviation of one, which is slightly different from the aim of normalization. This distinction is essential to understand the specific process being described.