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Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060709.

Integration Learning-Based User Activity Prediction—the Case of Wordle


Yipeng Miao1, Junhe Hou2, Yenan Xu3

Corresponding Author:
Junhe Hou

1School of Mathematics and Computer Science, Jilin Normal University, Siping, China

2High School Attached to Northeast Normal University, Changchun, China

3School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China


This paper introduces how to use machine learning methods to solve the user activity prediction problem of Wordle game. By analyzing time series forecasting methods, this paper introduces regression models and integrated learning models, and focuses on the AdaBoost regression algorithm, which is an integrated learning algorithm that can combine multiple weak models to build a strong model. In time series forecasting, the AdaBoost regression algorithm has high prediction accuracy and stability, so it is widely used in this field. In terms of research progress, there have been many applied studies in the field of time series forecasting, but this problem is still challenging. In the future, with the continuous development of machine learning and artificial intelligence algorithms, the time series prediction problem is expected to be studied and applied more deeply and extensively. This paper provides an overview of CART decision tree regression and AdaBoost regression algorithms as well as an introduction to application scenarios, which can provide useful reference suggestions for Wordle game developers.


Regression Decision Tree, AdaBoost, Integration Learning

Cite This Paper

Yipeng Miao, Junhe Hou, Yenan Xu. Integration Learning-Based User Activity Prediction—the Case of Wordle. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 59-63. https://doi.org/10.25236/AJCIS.2023.060709.


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