Research on word classification based on logistic regression and machine learning

Yudong Wang; Zhewen Huang; Yuhang Wang; Wenyi Chen; Xinrui Wei

doi:10.1117/12.2686778

28 July 2023 Research on word classification based on logistic regression and machine learning

Yudong Wang, Zhewen Huang, Yuhang Wang, Wenyi Chen, Xinrui Wei

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563Z (2023) https://doi.org/10.1117/12.2686778
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

Anagram is a popular daily puzzle offered by The New York Times. Players solve the puzzle by guessing a five-letter word in six or fewer attempts, with feedback for each guess. For this version, each guess must be an actual English word. The main purpose of this paper is to build a model to predict the future report results by taking the daily change in the number of report results, the change in date and the classification of difficulty as variables and taking "eerie" as an example. Classification of word difficulty is a problem of five inputs and seven outputs. In this paper, RSR model is established. Firstly, entropy weight method is used to evaluate 1try,2tries, 3tries,4tries, 5tries, 6tries. 7or more tries (X) to calculate the difficulty level of index weight division. Rank indexes from small to large, calculate the rank sum ratio, and finally make statistical regression ranking.

Citation Download Citation

Yudong Wang, Zhewen Huang, Yuhang Wang, Wenyi Chen, and Xinrui Wei "Research on word classification based on logistic regression and machine learning", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563Z (28 July 2023); https://doi.org/10.1117/12.2686778

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