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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Comparison of logistic regression and decision trees
 Raadová, Zuzana ; Voříšek, Jan (advisor) ; Komárek, Arnošt (referee)
In this thesis we describe a classification of the binary data. For discussing this problem we use two well-known methods - logistic regression and decision trees. These methods deal with the problem in different way, so our aim is to compare a successfulness of their predictions. At first a model of logistic regression is introduced and we show how to estimate its parameters using a method of maximum likelihood. Then we describe decision trees as one of the most popular classification tools. There are discussed older classic algorithms CART and C4.5 and also two new algorithms GUEST and CRUISE. The predictions of both of the methods are shown on a real data example.
Detailed record
Comparison of logistic regression and decision trees
 Raadová, Zuzana ; Voříšek, Jan (advisor) ; Komárek, Arnošt (referee)
In this thesis we describe a classification of the binary data. For discussing this problem we use two well-known methods - logistic regression and decision trees. These methods deal with the problem in different way, so our aim is to compare a successfulness of their predictions. At first a model of logistic regression is introduced and we show how to estimate its parameters using a method of maximum likelihood. Then we describe decision trees as one of the most popular classification tools. There are discussed older classic algorithms CART and C4.5 and also two new algorithms GUEST and CRUISE. The predictions of both of the methods are shown on a real data example.
Detailed record

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