The accuracy (error/loss) made by the model on the training data.

See Cost.

See Feature vector.

Learning techniques that group instances without a pre-specified dependent attribute. Clustering algorithms are usually unsupervised.

True negative rate (see Confusion matrix).

True positive rate (see Confusion matrix).

see Feature vector.

A mapping from unlabeled instances to a value within a predefined metric space (e.g., a continuous range).

The use of a learned model. Model deployment usually denotes applying the model to real data.

A structure and corresponding interpretation that summarizes or partially summarizes a set of data, for description or prediction. Most inductive algorithms generate models that can then be used as cl [..]