A matrix with training features ¹, as a rank-2 tensor:

\begin{equation*} X = \begin{pmatrix} \vphantom{\Big(} \vec{x}^1 & \cdots & \vec{x}^m \end{pmatrix} = \begin{pmatrix} x_1^1 & \cdots & x_1^m \\ \vdots & \ddots & \vdots \\ x_n^1 & \cdots & x_n^m \end{pmatrix} \in \mathbb{R}^{n \times m} \end{equation*}

where

• \(x^i\) is the \(i\)-th example
• \(x^i_j\) is the \(j\)-th feature of the \(i\)-th example

and

• \(n\) is the number of features
• \(m\) is the number of examples.