Normalization vs Standardization vs Scaling

1. Normalization:
  • Assume that in the given population (list of values) is in the range of [x_min, x_max]
  • Here we subtract min value of the population from all values so that the new range of population will be [0, (x_max - x_min)]
  • Now we will divide each element by (x_max - x_min) to convert the range of population into [0, 1]
  • Summary: Normalization actually used where we want to scale down the population to [0, 1]
2. Standardization:
  • Assume the same population again with range [x_min, x_max]
  • First, calculate the standard deviation (x_bar means the average of the population)
  • Now standardize the population with the following equation (μ = average of population that we computed above):
  • Summary: With standardization, we can transform the data into a range such that the new population has the mean (average) = 0 and standard deviation = 1.
3. Scaling:
  • Scaling is like multiplying or dividing each elements of the population with a constant value.
  • Consider the same population with [x_min, x_max] range.
  • Now we want to scale the whole population by 3 then the new range of the population will be [3*x_min, 3*x_max]
  • Summary: When we want to shrink or magnify the range to a given target range then we will use scaling.

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