Supervised Machine Learning Algorithms for the Prediction of Students Long Jump distance

Abstract

This study delves into the predictive analysis of factors affecting jump distance in students, employing various machine learning models. The models used include Linear Regression, Ridge Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, and Extra Trees Regression. The performance of these models was evaluated based on metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Squared Log Error (MSLE), R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and computation time. The analysis aims to identify the most effective model for predicting jump distances, thereby providing insights for athletic training and performance enhancement and to develop a reliable and accurate model that can assist physical education teachers in evaluating the jumping abilities of their students and designing appropriate training programs.