Prediction of Wordle report distribution results based on PSO-LBGM prediction model

Abstract

Accurate prediction of Wordle report headcount distribution is an important reference for Wordle's later word difficulty setting to expand the number of players. In order to increase the number of purchased products, the prediction model of the number of people distribution results is constructed by using Lightweight Gradient Boosting Machine (LightGBM), and PSO is used to optimize the hyperparameters of the LightGBM model. The historical data were preprocessed and the word attributes were extracted using unique thermal coding to build prediction models based on PSO-LightGBM, LightGBM and LSTM. The results show that the mean absolute percentage (MAPE) of the training and test sets predicted by PSO-LightGBM for (1, 2, 3, 4, 5, 6, X) is 0.531%, 0.410%, respectively. and the model was more accurate in predicting the number distribution results than LightGBM and LSTM models.