What's your next guess? Solution Word classification prediction model based on multi-dimensional feature Vector and MIMO-LSTM

In this article, the author explores the connection between word attributes and player scores. They create a multi-input, multi-output LSTM classification model to predict the distribution of attempts needed for a word, using a 12-dimensional feature vector with word roots and letter frequency. The model achieves a remarkable 95% accuracy. Additionally, the author develops a comprehensive classification model using system clustering and probability density distribution, incorporating clustering attributes. They use the entropy weighting method to assess word difficulty objectively and select optimal clustering through the elbow method. The probability density distribution function is then used to establish classification criteria. The article also uncovers new relationships and provides in-depth analysis of intriguing data features.