Feature scaling

Feature scaling transforms the numerical variables in a dataset to a common scale, so no single variable dominates the analysis or affects the performance of machine learning models more than it should.

Example

Suppose a dataset contains information about cars, including variables such as horsepower, ranging from 50 to 500, and fuel efficiency, ranging from 10 to 50 miles per gallon. This difference in scale can make a machine learning model more sensitive to horsepower, which may lead to biased or inaccurate predictions. By applying feature scaling techniques, such as normalization or standardization, you bring the variables to a common scale. That lets the model assess the importance of each variable more accurately and produce better predictions.