Streszczenie:
In this paper I have attempted to compare an efficiency and effectiveness of an
Ensemble learning techniques, based on binary classification of SARS-CoV-2 disease.
Boosting method and its applications will be the core of this thesis. I will compare
various Gradient Boosting derivatives; represent their features, benefits and drawbacks.
Also I will compare them with Neural Network. I have carry out my results and trained
each model constructed on more than 3 millions COVID-19 test cases, over 300k of
which had committed positive result. My results had illustrated the convenience and
weight of Boosting methods within the framework of binary classification on categorical
data, which means that attributes (variables) can take one of the limited number of
possible values. Due to simplicity and productivity these methods these methods can
be used in practice to recognize potential COVID cases and the symptoms that are
influencing the decision of the model.
