No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.
Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.
No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.
Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.
Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).
Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).
Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).
Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).