Feature engineering is the practice of using domain knowledge to select, transform, and construct input variables from raw data so that they better support predictive modeling. It includes operations such as normalization, encoding categorical values, creating interaction terms, and extracting meaningful signals. The persistence mechanism is the iterative modeling workflow: practitioners engineer features, evaluate model performance, and refine features based on empirical feedback. It is a cognitive and procedural craft enacted in data science pipelines. [formal: methodica | substrate: behavior | horizon: hours | explicit: yes | epoch: 1.02]
Accepted ontology entry
feature engineering
Feature engineering is the practice of using domain knowledge to select, transform, and construct input variables from raw data so that they better support predictive modeling. It includes operations such as normalization, encoding categor…
Definition
Why it is in scope
A human-made data preprocessing practice in machine learning: transforming raw variables into informative features that better capture the underlying relationship with the target variable, improving model performance through domain knowledge and statistical techniques.
Names and aliases
- feature engineeringen · CANONICAL
Relations from this entry
- cmrg0scos00ef2a1nklfvbk7xSERVES →
TESTED SERVES: feature engineering is built and maintained for the sake of machine learning — its designed purpose is to prepare input variables that further ML model training and inference. The note shows purpose by design: feature engineering pipelines are created specifically to advance ML systems, not incidentally.
Relations to this entry
- cmseehrm405al3vv3daniseir← INSTANCE_OF
Tested INSTANCE_OF: feature selection IS a specific kind of feature engineering. Feature engineering encompasses all techniques for creating, transforming, and selecting features from raw data; feature selection is the subset that specifically chooses a subset of existing features (as opposed to feature extraction which creates new ones).
- cmsebunmy05543vv3xqux72jr← INSTANCE_OF
Tested INSTANCE_OF: feature importance IS a specific kind of feature engineering. Feature engineering encompasses the full pipeline of creating, transforming, ranking, and selecting features. Feature importance scoring (computing quantitative measures of each feature's contribution to model performance) is a specific technique within that pipeline. Pinned sense: feature importance as the scoring/ranking subtask of the engineering process.
- cmsefagk705ca3vv3swbgluho← INSTANCE_OF
Feature extraction IS a specific kind of feature engineering: it automatically derives features from raw data (e.g. TF-IDF, autoencoders), while feature engineering is the broader discipline that includes extraction, selection, and creation. Specific→general, nearest kind is feature engineering.
Record identity
- Created
- Aug 4, 2026, 6:07 AM UTC
- Content hash
- 2ffae7d60e61d13449ffd45fac306b60bf90127e81db4284297e1030b4f6f4fc