Basics of classification problems

Classifiers identify the class of a new objective, based on knowledge that's been extracted from a series of samples (dataset). Starting from a dataset, a classifier extracts a model, which is then used to classify the new instances. Examples of classifiers are as follows:

• The texts classifier: This classifier is able to say whether a piece of text is relevant to a pre-established topic, based only on the terms that appear
• The image classifier: This is a classifier that, given some points of a simple drawing, is able to reconstruct the underlying pattern
• The medical classifier: This is a classifier that, from a sufficient amount of clinical data, is able to find out whether a patient is suffering from a certain disease, or what the severity of the disease is

Therefore, the classifier is a system with some characteristics that allow you to identify the class of the sample examined. In different classification methods, groups are called classes. The goal of a classifier is to establish the classification criterion to maximize performance. The performance of a classifier is measured by evaluating the capacity for generalization. Generalization means attributing the correct class to each new experimental observation. The way in which these classes are identified discriminates between the different methods that are available.