Real-World Machine Learning by Henrik Brink

Author:Henrik Brink
Language: eng
Format: epub
Publisher: Manning Publications

Feature engineering is the process of applying mathematical transformations to raw data to create new input features for ML modeling. The transformations can range from simple to extremely complex.

Feature engineering is valuable for the following five reasons:

It can create features that are more closely related to the target variable.

It enables you to bring in external data sources.

It allows you to use unstructured data.

It can enable you to create features that are more interpretable.

It gives you the freedom to create lots of features and then choose the best subset via feature selection.

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