Featuresshapes

Featuresshapes is a term used in data science and machine learning to describe the geometric and structural properties of features as they are arranged in a dataset or input to a model. It is not a standard label in major reference works, but it serves as a concise way to discuss how features occupy space in a representation and how that space influences learning.

Featuresshapes encompasses the shape of feature tensors (1D vectors, 2D matrices, 3D tensors) and the distribution and relationships among features. It covers flat tabular features, dense or sparse vectors, as well as higher-rank data such as sequences (time steps), images or image-like arrays, and graph-structured features. The concept also includes invariances and correlations that affect how models can exploit interactions among features and how preprocessing should respect dimensionality and alignment.

Applications: In feature engineering and model design, awareness of featuresshapes helps choose appropriate preprocessing, encoding, and architectures. For example, feed-forward networks expect flat vectors, while convolutional layers exploit spatial shapes; recurrent or transformer models handle sequences with temporal dimensions; graph neural networks operate on graph-shaped feature structures. Shape-aware validation helps catch alignment errors and informs dimensionality reduction and batching decisions.

Examples: A dataset with 1000 samples and 64 features has the shape 1000x64; a color image dataset with 28x28x3 has shape 28x28x3; a time-series dataset with 1000 sequences of length 100 and 16 features per step has shape 1000x100x16.

See also: feature engineering, tensor shape, data shape.