English Word Difficulty Classifier Based on Random Forest Model

Recently, Wordle has become popular worldwide as a daily puzzle game launched by the New York Times. Players try to solve guessing five-letter word in six tries or less. According Wordle's statistical data, this paper first uses K-means algorithm cluster difficulty of solution words quantify English and analyzes accuracy scientificity clustering results. Then, Random Forest model classify into three categories: ‘easy’, ‘normal’ ‘hard’. The results show that classification on training set test reaches 0.972 0.978 respectively.