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Machine learning classification

The classification problem is often found in machine learning, in data science, and data mining.The task of classification is the assignment of objects to one of the previously known classes according to certain rules. Our world-class fintech and cloud engineering team with a solid background of practice that combines consulting, strategy, design and engineering at scale, can help with outsource machine learning development and provide advisory services.


In trade

Classification of customers and goods allows you to optimize marketing strategies, stimulate sales, and reduce costs.

In the field of telecommunications

Classification of subscribers allows you to determine the level of loyalty, develop loyalty programs.

Areas of application of the classification problem

Features of the classification problem

Two types of classification problem

The classification is divided into two types: multiclass and binary. The meaning of a binary classification is a choice of two options (classes), and a multiclass one is a choice of several options (classes).

Analytical models solving the classification problem

Analytical models that solve the classification problem are called classifiers. A classifier is an algorithm that correlates some input data with one or more classes. Unlike clustering algorithms, these classes must be predefined.

Methods for solving the classification problem

Common methods for solving the classification problem include neural networks; logistic and probit regression; decision trees; nearest neighbor method; support vector machines; discriminant analysis.

In artificial intelligence and machine learning, the classification problem is the problem of dividing a set of observations (objects) into groups, called classes, based on the analysis of their formal description. In classification, each observation unit is assigned to a specific group or nominal category based on some qualitative property. 

In mathematical statistics, classification problems are also called discriminant analysis problems.

That is, the machine sorts data according to the necessary categories: clothes – by colors, seasons or fabrics, books – by genres, authors, languages ​​of writing, sauces – according to the degree of pungency, letters – by personal or work focus, spam, etc.

In business, you can classify, for example, customers: by the number of purchases, the frequency of visits to the site, and purchasing habits. For example, letters from a supermarket chain work according to such a system: each participant of the loyalty program receives offers with discounts on goods that they most often buy. Also, a similar system can be used by banks, which need to determine the likelihood that the loan will be repaid based on the general portrait of the loan applicant.

In machine learning, the classification problem is solved using supervised learning, since the classes are predefined and class labels are specified for the examples of the training set. The classification problem is one of the basic problems of applied statistics and machine learning, as well as artificial intelligence in general. This is due to the fact that the classification is one of the most understandable and easy-to-interpret technologies for data analysis, and the classification rules can be formulated in natural language.

An additional product of classification by specified parameters is the ability to select everything that does not fit into the standard classes. For example, if we are talking about medicine, the selected fragment can be any deviation from the norm: thickening, rupture, neoplasm, overestimated or underestimated test values. If we are talking about financial markets, then non-standard indicators can give out insider players.

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