What is normalization?
The process of scaling numeric data to a common range, usually between 0 and 1, to prevent differences in scales for different features
normalization explained in plain English
Normalization is a technique used to adjust the values of different data features to be on the same scale, which helps in improving the performance and accuracy of machine learning models
Analogy
Think of normalization like adjusting the volume levels of different music tracks so that they can be played together without any track being too loud or too quiet
Example
For example, when building a model to predict house prices, the features might include the number of bedrooms, square footage, and location, which have different units and scales, so normalization is used to scale these features to a common range
How is normalization used?
Normalization is used in data preprocessing for machine learning models, especially when the features have different units or scales, to ensure that all features are treated equally by the model
Common misconceptions about normalization
One common misconception is that normalization is the same as feature scaling, but while related, they are not exactly the same, as normalization typically refers to scaling to a specific range, usually between 0 and 1
History
The concept of normalization has been around for decades and is widely used in various fields, including statistics, signal processing, and machine learning
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