Data in the real world can be extremely messy and chaotic. It doesn’t matter if it is a relational SQL database, Excel file or any other source of data. Despite being usually constructed as tables where each row (called sample) has its own values corresponding to a given column (called feature), the data may be hard to understand and process. To make the reading of the data easier for our machine learning models and thanks to that increase its performance, we can conduct feature engineering.
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Feature engineering - tell your model what to look at
November 30, 2018 by Paweł Grabiński Feature engineering