의사결정트리, 랜덤포레스트, 그리고 그래디언트부스팅 모델에 기반한 운동선수의 부상 예측

초록

This study compares and analyzes the performance and variable importance of injury prediction by applying tree-based algorithms such as Decision Tree, Random Forest, and Gradient Boosting among supervised learning models of machine learning. Rule-based prediction is performed by recursively dividing data based on Gini Impurity in the decision tree. Random forest trains multiple decision trees through random sampling and performs final prediction through voting. Gradient boosting learns weak classifiers sequentially and improves prediction performance by reducing errors in previous steps. In this paper, for ease of interpretation, the learning itself is fixed as depth=3. Even if the learning is deeper, the structural picture of the three models is shown only up to max_depth=3. The final prediction tree of the random forest is an explanatory tree that approximates the majority judgment of the entire forest. In other words, their final prediction tree cannot show hundreds of trees in a forest as a single tree, so they generally used a surrogate tree technique. In the gradient boosting tree, the 300th tree was output. The concepts of Accuracy, Precision, Recall, and F1-Score are important indicators for evaluating the performance of artificial intelligence models. In this study, the performance of each model was evaluated based on Accuracy, Precision, Recall, and F1 score. In addition, analytical differences were analyzed by comparing the variable importance derived from each model.

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