The various terminologies used in Machine Learning are -

Features

A set of variables that carry discriminating and characterizing information about the objects under consideration.

Feature Vector

A collection of r features ordered in a meaningful way into an n-dimensional column vector that represents the signature of the object to be identified.

Feature Space

Feature space is an n-dimensional space in which the feature vectors lie. An n-dimensional vector in an n-dimensional space constitutes a point in that space.

Class

The category to which a given object belongs to.

Decision Boundary

A boundary in the n-dimensional feature space that separates patterns of different classes from each other.

Classifier

An algorithm that adjusts its parameters to find the correct decision boundaries through a learning algorithm using a training dataset such that a cost function is minimized.

Error

Incorrect labeling of data by the machine learning algorithm.

Training Performance

The ability/performance of the machine learning algorithm to correctly identify the classes or target values of the training data, which it has already seen.

It is not a good indicator of generalization performance.

Generalization (Test Performance)

Generalization is the ability/performance of the machine learning algorithm to identify the classes or target values of previously unseen data.