Which of the following normalization techniques reduces a numerical variable to a range between 0 and 1?

Which of the following normalization techniques reduces a numerical variable to a range between 0 and 1?

• A. Z-score normalization
• B. Min-max normalization
• C. Decimal scaling
• D. Log normalization

Answer: (B)

Min-max normalization is a technique that transforms a numerical variable so that its values fall within a specified range, typically between 0 and 1. This is accomplished by applying the formula: \[ X' = \frac{X - X_{min}}{X_{max} - X_{min}} \] where \(X\) is the original value, \(X_{min}\) is the minimum value of the dataset, and \(X_{max}\) is the maximum value of the dataset. By using this formula, each value in the dataset is rescaled proportionally based on the minimum and maximum values, ensuring that the entire dataset is compressed into the new range of 0 to 1. This technique is particularly useful in data preprocessing, especially when preparing data for machine learning algorithms that are sensitive to the scale of input features. By normalizing data in this way, one can avoid biases that might arise from features with varying scales. Other normalization techniques, such as z-score normalization, do not bound the data to a range of 0 to 1, rather they standardize it to have a mean of 0 and standard deviation of 1, focusing on the distribution of the data because it assumes a normal distribution. Similarly

Min-max normalization is a technique that transforms a numerical variable so that its values fall within a specified range, typically between 0 and 1. This is accomplished by applying the formula:

[

X' = \frac{X - X_{min}}{X_{max} - X_{min}}

]

where (X) is the original value, (X_{min}) is the minimum value of the dataset, and (X_{max}) is the maximum value of the dataset. By using this formula, each value in the dataset is rescaled proportionally based on the minimum and maximum values, ensuring that the entire dataset is compressed into the new range of 0 to 1.

This technique is particularly useful in data preprocessing, especially when preparing data for machine learning algorithms that are sensitive to the scale of input features. By normalizing data in this way, one can avoid biases that might arise from features with varying scales. Other normalization techniques, such as z-score normalization, do not bound the data to a range of 0 to 1, rather they standardize it to have a mean of 0 and standard deviation of 1, focusing on the distribution of the data because it assumes a normal distribution. Similarly