FeatureSpace
Featurespace refers to the mathematical space that contains all possible feature vectors used to represent data for a given problem. In machine learning and data analysis, each data instance is a point in this space, with coordinates corresponding to the values of its features. When a dataset has d features, the featurespace is typically modeled as a d-dimensional vector space, often denoted as R^d. The structure and geometry of this space underpin many learning algorithms and their ability to distinguish patterns.
Representations and preprocessing can change the effective featurespace. Numerical features may be scaled or standardized, while
The geometry of featurespace is central to many algorithmic decisions. Distance metrics define similarity between points,
Common examples illustrate featurespace in action: image data where each pixel is a feature; text data transformed
See also: feature engineering, dimensionality reduction, distance metric, kernel methods.