Wordle character prediction model based on linear XGBoost

被引：0

作者：

Huo, Wei ^{[1
]}

Xiao, Yu ^{[1
]}

Lu, Fangyin ^{[2
]}

Ji, Haofu ^{[1
]}

Liu, Tao ^{[3
]}

机构：

[1] Northeastern Univ Qinhuangdao, Sch Control Engn, Qinhuangdao, Hebei, Peoples R China

[2] Northeastern Univ Qinhuangdao, Sch Resources & Mat, Qinhuangdao, Hebei, Peoples R China

[3] Northeastern Univ Qinhuangdao, Sch Math & Stat, Qinhuangdao, Hebei, Peoples R China

来源：

PROCEEDINGS OF 2024 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION NETWORKS AND MACHINE LEARNING, CNML 2024 | 2024年

关键词：

LSTM model; prediction; XGBoost model; classification;

D O I：

10.1145/3701047.3701049

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper focuses on predicting four questions posed by the New York Times about Wordle. We use a least-square linear model and a nonlinear XGBoost model to create a regression model that predicts the percentage correlation for each of the future days (1, 2, 3, 4, 5, 6, X). Then, we combine these two models to form a linear XGBoost model, which combines the advantages of linear and nonlinear models and can greatly enhance the accuracy of the prediction's fit.

引用

页码：7 / 10

页数：4

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