Making everything equally average.
Standardization rescales data to have a mean of 0 and a standard deviation of 1, making different features comparable.
Imagine you have a basket of fruits where apples are measured in pounds and oranges in ounces—it's hard to compare them directly. Standardization is like converting everything to the same unit, so you can see which fruit is actually bigger relative to its own kind. It makes all your data play fair, no matter how big or small the original numbers were.
Standardization is crucial for many machine learning algorithms (like SVM or k-means) that assume all features are on a similar scale; otherwise, features with larger values can dominate the results. You encounter it in any data preprocessing pipeline, especially when combining variables like income and age in a model.
A common mistake is thinking standardization makes data normally distributed—it doesn't; it only shifts and scales the data. Standardization changes the mean and variance, not the shape of the distribution.
Standardization, also known as Z-score normalization, transforms data by subtracting the mean and dividing by the standard deviation for each feature. This yields a distribution with a mean of 0 and a standard deviation of 1, preserving the shape of the original distribution. It is a linear transformation used to center and scale data, often applied before distance-based or gradient-descent algorithms.