Tips for Effective Feature Engineering in Machine Learning
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Feature engineering is an important step in the machine learning pipeline. It is the process of transforming data in its native format into meaningful features to help the machine learning model learn better from the data.
If done right, feature engineering can significantly enhance the performance of machine learning algorithms. Beyond the basics of understanding your data and preprocessing, effective feature engineering involves creating interaction terms, generating indicator variables, and binning features into buckets.
These techniques help extract relevant information from the data and help build robust machine learning solutions. In this guide, we’ll explore these feature engineering techniques by