What is the difference between normalization and standardization?

What is the difference between normalization and standardization?

Normalization and standardization are both techniques used to scale data. Normalization typically rescales the data to a range of [0, 1], while standardization transforms data to have a mean of 0 and a standard deviation of 1.

When should I use normalization instead of standardization?

You should use normalization when your data has a bounded range and you want to maintain that range. It's often used in algorithms that require a specific range, like neural networks.

Can you give me an example of when to use standardization?

Standardization is useful when your data follows a Gaussian distribution. For instance, in algorithms like Support Vector Machines or Logistic Regression, standardizing the features can improve performance.

Are there any drawbacks to using these techniques?

Yes, normalization can distort relationships in the data if outliers are present, while standardization may not be effective if the data is not normally distributed.