Feature scaling is a method used in machine learning to normalize the range of independent variables or features of data. It is performed during the data pre-processing to handle highly varying magnitudes or values or units. The goal of feature scaling is to ensure that all features are on a comparable scale and have comparable ranges. If feature scaling is not done, then a machine learning algorithm tends to weigh greater values higher and consider smaller values as lower values, regardless of the unit of the values.

There are several reasons why feature scaling is important in machine learning:

• Algorithm performance improvement: When the features are scaled, several machine learning methods, including gradient descent-based algorithms, distance-based algorithms (such as k-nearest neighbors), and others, converge faster and perform better.

• Avoidance of domination by larger scale features: The magnitude of the features has an impact on many machine learning techniques. Larger scale features may dominate the learning process and have an excessive impact on the outcomes. Scaling the features can avoid this problem and make sure that each feature contributes equally to the learning process.

There are several methods available for feature scaling, including normalization, standardization, and rescaling. Normalization is used when we want to bound our values between two numbers, typically between or . Rescaling (min-max normalization) scales the range of features to scale the range in . The choice of scaling method depends on the data and the machine learning algorithm being used.