In machine learning and statistics, classification is the problem of identifying which of a set of categories a new observation belongs to, on the basis of a training set of data containing observations whose category membership (label) is known.
In machine learning and statistics, classification refers to the problem of predicting category memberships based on a set of pre-labeled examples. It is thus a type of supervised learning.
Some of the most important classification algorithms are support vector machines svm, logistic regression, naive Bayes, random forest random-forest and artificial neural networks neural-network.
When we wish to associate inputs with continuous values in a supervised framework, the problem is instead known as regression. The unsupervised counterpart to classification is known as clustering (or cluster analysis), and involves grouping data into categories based on some measure of inherent similarity.
