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Concept
Feature engineering
Summary
Feature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
Other examples of features in physics include the construction of dimensionless numbers such as Reynolds number in fluid dynamics; then Nusselt number in heat transfer; Archimedes number in sedimentation; construction of first approximations of the solution such as analytical strength of materials solutions in mechanics, etc.
Features vary in significance. Even relatively insignificant features may contribute to a model. Feature selection can reduce the number of features to prevent a model from becoming too specific to the training data set (overfitting).
Feature explosion occurs when the number of identified features grows inappropriately. Common causes include:
Feature templates - implementing feature templates instead of coding new features
Feature combinations - combinations that cannot be represented by a linear system
Feature explosion can be limited via techniques such as: regularization, kernel methods, and feature selection.
Automation of feature engineering is a research topic that dates back to the 1990s. Machine learning software that incorporates automated feature engineering has been commercially available since 2016. Related academic literature can be roughly separated into two types:
Multi-relational decision tree learning (MRDTL) uses a supervised algorithm that is similar to a decision tree.
Deep Feature Synthesis uses simpler methods.
MRDTL generates features in the form of SQL queries by successively adding clauses to the queries. For instance, the algorithm might start out with
SELECT COUNT(*) FROM ATOM t1 LEFT JOIN MOLECULE t2 ON t1.mol_id = t2.mol_id GROUP BY t1.mol_id
The query can then successively be refined by adding conditions, such as "WHERE t1.
Official source
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