Prediction and Classification Models of Wordle Data Set Based On ARMA, XGBoost

Xiangcheng Meng; Chuanzhen Wang; Weichen Zang

doi:10.54097/pj5kg994

Authors

Xiangcheng Meng
Chuanzhen Wang
Weichen Zang

DOI:

https://doi.org/10.54097/pj5kg994

Keywords:

Natural language, Prediction, COF, ARMA, XGBoost.

Abstract

This study intends to use machine learning method to solve the problem of predicting the number of word guessing and the difficulty of word guessing in natural language. Firstly, this study smoothed the data according to the COF method. According to the images of the sample autocorrelation function and the sample partial autocorrelation function, the ARMA model is constructed and the coefficient of the AMRA model is calculated, and the number of word contest people on March 1, 2023 is predicted to drop to [18002,20622]. Second, based on the seven attributes of the word, the XGBoost classification model is used to predict the value of the first attempt, and then the XGBoost prediction model is used to predict the results of the other attempts. Eerie distribution of different attempts to {0,8.890, 17.650, 28.191, 32.451, 11.349, 1.483}. The average accuracy of the model on the test set is 79.4%.

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References

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