Summary of A Random Forest-based Prediction Model For Turning Points in Antagonistic Event-group Competitions, by Zishuo Zhu
A Random Forest-based Prediction Model for Turning Points in Antagonistic Event-Group Competitions
by Zishuo Zhu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper develops a novel prediction model based on Random Forest to forecast the turning point of an antagonistic event-group competition. This model is designed to provide real-time feedback on athletes’ state information during the competition, which can help analyze changes in the competition situation. The approach begins by establishing a quantitative equation for competitive potential energy and then uses a dynamic combination of weights method to calculate its value. A grid search method and KM-SMOTE algorithm are employed to optimize the model’s performance. Experimental results demonstrate that the proposed model effectively predicts the turning point, achieving a recall rate of 86.13% in the test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to predict when an event-group competition will change direction. This helps us understand what’s happening during the competition and make predictions about who will win or lose. The approach uses math equations to describe how competitive energy changes over time, and then uses computer algorithms to analyze this data. The results show that this method is very good at predicting when a competition will turn around, which can be helpful for athletes, coaches, and fans. |
Keywords
» Artificial intelligence » Grid search » Random forest » Recall
