Feature scaling

Acting as a gentle equalizer, feature scaling is the process of transforming the numerical variables in a dataset to a common scale, ensuring that no single variable dominates or skews the analysis or performance of machine learning models, much like balancing the sound of various instruments in a musical ensemble.

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). The difference in scale between these variables might cause a machine learning model to be more sensitive to the horsepower variable, potentially leading to biased or inaccurate predictions. By applying feature scaling techniques, such as normalization or standardization, the variables can be brought to a common scale, allowing the model to more accurately assess the importance of each variable and make better predictions.