Machine learning based wordle difficulty analysis

Pujun Su; Shanqi Wang; Ting Wang

doi:10.1117/12.3006393

10 October 2023 Machine learning based wordle difficulty analysis

Pujun Su, Shanqi Wang, Ting Wang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279924 (2023) https://doi.org/10.1117/12.3006393
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Wordle is a popular game, and it is necessary to analyze the massive amount of data to help improve the game. For the prediction problem of the number of reports, the three-times exponential smoothing prediction model with better prediction results was selected. This study collected the relationship between word attributes and the percentage of the number of reports in the difficult mode. After data preprocessing, the RMAE test and 𝑅² test were performed on the prediction effects of the two models; For the problem of predicting the percentage of attempts (1, 2, 3, 4, 5, 6, X), a BP neural network model optimized using a genetic algorithm was built and the results showed that the confidence level of the model was 0.742, which tested by prediction of the percentage distribution of words corresponding to the number of attempts per day; For the word difficulty prediction grading problem, this study built a hierarchical clustering model and solved the optimal number of clusters K=4 for word classification according to the elbow rule, and obtained the classification results using the improved BP neural network model to train the data. The results showed that the accuracy of the model was 0.831. This study can improve Wordle, enhance the user's sense of game experience, and attract more people to participate in the game.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pujun Su, Shanqi Wang, and Ting Wang "Machine learning based wordle difficulty analysis", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279924 (10 October 2023); https://doi.org/10.1117/12.3006393

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KEYWORDS

Data modeling

Neural networks

Education and training

Genetic algorithms

Mathematical optimization

Machine learning

Neurons

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