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2019
In the given work, one proposes robust modifications of the machine learning algorithm, Random Forest, which are used to solve classification and regression problems. Random Forest is implemented by constructing a set of Decision Trees and averaging their estimates. For small samples, the estimates cannot be regarded as accurate.
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In the given work, one proposes robust modifications of the machine learning algorithm, Random Forest, which are used to solve classification and regression problems. Random Forest is implemented by constructing a set of Decision Trees and averaging their estimates. For small samples, the estimates cannot be regarded as accurate.
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The purpose of the graduate qualification work is to solve the problem of dimensionality of the coefficient matrix in vector autoregression models as an effective method of short-term forecasting. Methods used in preparation for the research and its implementation: deduction, induction, historical method, method of analogies, analysis and synthesis ...
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