linearly separable

The glossary is being gradually proof checked, but currently has many typos and misspellings.

Two sets of data are linearly separable if there is a straight line (or hyperplane in higher dimensions) that separates them. In other words there is a linear combination (a_i) of the features and a threshold T such that
a₁d₁ + a₂d₂ + ... + a_nd_n
is less than T for data items (d_i) in the first datasets and greater than T for the second. In such cases, the hyperplane acts as a classifier.

Used in Chap. 6: page 75; Chap. 7: page 92

Also known as linear separability