Which scaling method is typically used for transforming features into the range of [0, 1]?

Min-Max Scaling is used to transform features into a specific range, typically [0, 1]. It works by subtracting the minimum value of the feature and then dividing by the range of that feature (which is the difference between the maximum and minimum values). This method ensures that the smallest value of the feature maps to 0 and the largest to 1, while all other values are proportionally scaled within that range.

This scaling technique is particularly useful when working with algorithms that require features to be within a bounded interval or when the distribution of the data is not Gaussian. It’s essential in cases where the model's performance is sensitive to the scale of the input data.

Other scaling methods, such as Standard Scaling, adjust the features based on their mean and standard deviation, which centers the data around zero with a unit variance but does not constrain it between 0 and 1. Logarithmic Scaling transforms data to bring down the effect of extreme values but also does not confine the result within a specific range. MaxAbs Scaling, on the other hand, scales features by their maximum absolute value but also does not restrict the output to the [0, 1] interval.