Terminologies used in Machine Learning
The various terminologies used in Machine Learning are -
A set of variables that carry discriminating and characterizing information about the objects under consideration.
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 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.
The category to which a given object belongs to.
A boundary in the n-dimensional feature space that separates patterns of different classes from each other.
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.
Incorrect labeling of data by the machine learning algorithm.
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.