Using the Decision Tree Model Method for Data Analysis

被引：0

作者：

Tynchenko, Vadim S. ^{[1
,2
]}

Nelyub, Vladimir A. ^{[1
,3
]}

Gantimurov, Andrei P. ^{[1
]}

Kravtsov, Kirill I. ^{[4
]}

Mikhalev, Anton S. ^{[5
]}

机构：

[1] Bauman Moscow State Tech Univ, Artificial Intelligence Technol Sci & Educ Ctr, Moscow 105005, Russia

[2] Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia

[3] Far Eastern Fed Univ, Vladivostok, Russia

[4] Reshetnev Siberian State Univ Sci & Technol, Krasnoyarsk 660037, Russia

[5] Siberian Fed Univ, Krasnoyarsk 660041, Russia

来源：

PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2024, VOL 6 | 2024年 / 1002卷

关键词：

Decision tree; Data analysis; Machine learning; Predictive modeling; Classification;

D O I：

10.1007/978-981-97-3299-9_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The publication introduces an overview of modern data analysis methods and technologies used in forecasting tasks, and considers key aspects of using data analytics in the context of professional football. In a number of regions of the world professional football is widespread, in which participation in football games becomes the main profession of players. On this basis, professional football clubs are organized and playing in them becomes part of a player's professional career. Particular attention is paid to the application of data analysis techniques to predict various factors as well as to assess the quality of the predictions.

引用

页码：149 / 157

页数：9

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