Feature Engineering for Machine Learning

Feature engineering techniques for machine learning are a fundamental topic in machine learning, but one that is often overlooked or deceptively simple.

Feature engineering is the creation, transformation, extraction, and selection of entities, also known as variables, that are most conducive to creating an accurate ML algorithm. These processes involve:

Feature creation: Feature creation involves identifying the variables that will be most useful in the predictive model. It is a subjective process that requires human intervention and creativity. Existing features are mixed by addition, subtraction, multiplication, and ratio to create new derived features that have greater predictive power.

Transformations: Transformation involves the manipulation of predictor variables to improve model performance; p. e.g., ensuring the model is flexible in the variety of data it can ingest; ensure that the variables are on the same scale, which makes the model easier to understand; improving accuracy; and avoid calculation errors by ensuring that all characteristics are within an acceptable range for the model.

Feature extraction: Feature extraction is the automatic creation of new variables by extracting them from raw data. The purpose of this step is to automatically reduce the volume of data into a more manageable set for modeling. Some feature extraction methods include cluster analysis, text analysis, edge detection algorithms, and principal component analysis.

Feature Selection: Feature selection algorithms essentially analyze, judge, and rank various features to determine which features are irrelevant and should be removed, which features are redundant and should be removed, and which features are most useful for the model and must be ranked.