id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-30996	Wang, Saijie; He, Dongyang; Shan, Yufei; Li, Hongjia	Olympic Medal Count Prediction Model for Various Countries based on LSTM and Supervised Machine Learning	2025	5	.pdf	application/pdf	2343	109	55	4. Results Given that our built-in model is a regression supervised learning model, we use the Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-squared (R2) goodness-of- fit to evaluate the model's prediction results. Through the Random Forest model, we can deeply explore the potential relationships between features and medal counts, thereby effectively predicting the number of medals for each country.	cache/fcis-30996.pdf	txt/fcis-30996.txt