In the master's thesis we will examine the Generalized Linear Model (GLM) and its assumptions. As the name already implies, the GLM is a generalization of the linear model. The most important generalization is the assumption that the random variable is not necessarily distributed normally but belongs to the family of exponential distribution functions.
In the second part of the master's thesis, we will look at machine learning and compare the methods of machine learning with GLM. As we know, we are living in the era of data. The only solution for processing and making sense of the vast amount of available data, is machine learning and data mining. Scientists say that some methods of machine learning mimic the decision-making of humans. The theme of this master's thesis is thus an attempt to replace GLM with one of the machine learning methods. We will look at how we can build decision trees and what parameters we have, how artificial neural networks are built, how they are related to biological neural networks, and how we can choose the best model in
machine learning. Finally, an example of the calculation of four models (decision tree, random forest, neural networks and cascade model) as well as a comparison of
these models with GLM in programming language R is presented.