FeatureSet

A featureset is the collection of input features used to represent data for analysis or predictive modeling. It represents the variables that describe each instance in a dataset and is distinct from the target variable that models aim to predict. In practice, a featureset can include raw measurements, categorical attributes, textual information, and engineered or derived features created to improve model performance.

A featureset is often transformed into a feature vector or matrix before modeling. Each instance becomes a row, and each feature becomes a column. Features can be numeric, categorical (often encoded), or binary, and may undergo preprocessing such as normalization, scaling, imputation of missing values, or encoding. Derived features—such as ratios, interactions, or aggregations—are commonly added to enrich the featureset.

The creation and maintenance of a good featureset involve feature engineering, feature selection, and quality control. Feature engineering seeks informative representations tailored to the problem domain, while feature selection aims to reduce dimensionality and remove redundant or noisy features. In production environments, feature stores or feature pipelines are used to manage and reuse features across models, ensuring consistency and governance.

Common uses of a featureset include supervised learning, where it forms the input to algorithms, and exploratory data analysis, where features help reveal patterns and relationships. The term is sometimes used interchangeably with feature set or simply features, though in practice, a featureset emphasizes the collection as a unit prepared for modeling, rather than the individual features themselves.