Importance of Feature Transformation - Amazon Machine Learning

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Importance of Feature Transformation

Consider a machine learning model whose task is to decide whether a credit card transaction is fraudulent or not. Based on your application background knowledge and data analysis, you might decide which data fields (or features) are important to include in the input data. For example, transaction amount, merchant name, address, and credit card owner's address are important to provide to the learning process. On the other hand, a randomly generated transaction ID carries no information (if we know that it really is random), and is not useful.

Once you have decided on which fields to include, you transform these features to help the learning process. Transformations add background experience to the input data, enabling the machine learning model to benefit from this experience. For example, the following merchant address is represented as a string:

"123 Main Street, Seattle, WA 98101"

By itself, the address has limited expressive power – it is useful only for learning patterns associated with that exact address. Breaking it up into constituent parts, however, can create additional features like "Address" (123 Main Street), "City" (Seattle), "State" (WA) and "Zip" (98101). Now, the learning algorithm can group more disparate transactions together, and discover broader patterns – perhaps some merchant zip codes experience more fraudulent activity than others.

For more information about the feature transformation approach and process, see Machine Learning Concepts.