Which technique involves scaling features so that the lowest value is 0 and the highest is 1?

Which technique involves scaling features so that the lowest value is 0 and the highest is 1?

• A. Standardization
• B. Normalization
• C. Encoding
• D. Normalization

Answer: (B)

The technique that involves scaling features so that the lowest value is 0 and the highest is 1 is known as normalization. This process is often used in data preprocessing to bring different features into a similar scale. Normalization typically applies the formula: \[ \text{Normalized value} = \frac{(X - X_{min})}{(X_{max} - X_{min})} \] Where \( X \) is the original value, \( X_{min} \) is the minimum value of the feature, and \( X_{max} \) is the maximum value of the feature. By applying this technique, you ensure that all the feature values are rescaled to fit within a range of 0 to 1, which can be particularly beneficial for algorithms that are sensitive to the scale of data, such as those based on Euclidean distance. In contrast, standardization scales features to have a mean of 0 and a standard deviation of 1, which is not the same as normalizing the data to a range between 0 and 1. Encoding refers to converting categorical variables into a numerical format, which is unrelated to the scaling of feature values. The repetition of the term "normalization" may suggest a typ

The technique that involves scaling features so that the lowest value is 0 and the highest is 1 is known as normalization. This process is often used in data preprocessing to bring different features into a similar scale. Normalization typically applies the formula:

[ \text{Normalized value} = \frac{(X - X_{min})}{(X_{max} - X_{min})} ]

Where ( X ) is the original value, ( X_{min} ) is the minimum value of the feature, and ( X_{max} ) is the maximum value of the feature. By applying this technique, you ensure that all the feature values are rescaled to fit within a range of 0 to 1, which can be particularly beneficial for algorithms that are sensitive to the scale of data, such as those based on Euclidean distance.

In contrast, standardization scales features to have a mean of 0 and a standard deviation of 1, which is not the same as normalizing the data to a range between 0 and 1. Encoding refers to converting categorical variables into a numerical format, which is unrelated to the scaling of feature values. The repetition of the term "normalization" may suggest a typ