Machine learning methods for classification problems

Abstract

Machine learning means the application of computer algorithms onto a dataset to discover structure. The term ‘machine’ indicates that a computer (i.e. machine) is usually needed to conduct the algorithms (large datasets, lots of calculations). The term ‘learning’ indicates that one would like to formulate some system from the data. The discovered structure is intended to be applied beneficially in the future. In this contribution, the author focused on classification problems with predefined classes, taking a dataset with n statistical units. Each unit belongs to one of k classes. Let Y denote the class. In addition to class Y, we observe a vector of further characteristics X = (X1, ..., X_p). The structure of interest is function fallowing good predictions of Y based on the input characteristics X, i.e. requiring that f(X_1, ..., X_p) = Y often holds. Thus the found function f can be employed to predict class Y for new statistical units based on known values for the input variables X_1, ..., X_p. In practice, classification problems often occur. For example, let us consider a bank that offers loans. The classes here may be ‘correct repayment’ and ‘problems with repayment’. Typical input characteristics are income, savings, real estate, duration of employment contract, further loans, age, and family status. The bank is interested in a prediction of