Feature scaling is essential for machine learning algorithms that calculate distances between data. … Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions do not work correctly without normalization.

When should you scale your data?

You want to scale data when you’re using methods based on measures of how far apart data points, like support vector machines, or SVM or k-nearest neighbors, or KNN. With these algorithms, a change of “1” in any numeric feature is given the same importance.

What does it mean to scale a dataset?

Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. … Data scaling is a recommended pre-processing step when working with deep learning neural networks. Data scaling can be achieved by normalizing or standardizing real-valued input and output variables.