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Classification and Regression Forests
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
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Classification and Regression Forests.
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
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Lesk a bída optimálních stromů
Savický, Petr ; Klaschka, Jan
Optimal classification trees have the smallest error on training data, given the number of leaves. Previous experiments suggest that the generalization properties of the optimal trees might be consistently at least as good as these of the trees grown by classical methods. The result presented in current paper demonstrate, however, that for some classification problems the optimal trees are outperformed by the classical ones.
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