Summary of Research on Effectiveness Evaluation and Optimization Of Baseball Teaching Method Based on Machine Learning, by Shaoxuan Sun et al.
Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning
by Shaoxuan Sun, Jingao Yuan, Yuelin Yang
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 research aims to develop a machine learning model to predict students’ comprehensive scores in baseball training, evaluating the effectiveness of current methods and providing targeted optimization suggestions. A variety of models, including K-Neighbors Regressor and Gradient Boosting Regressor, are trained on characteristics such as hitting times, running times, and batting. The results show that these models achieve high accuracy and stability, outperforming other approaches. Feature importance analysis reveals that cumulative hits and runs are key factors affecting overall performance. Based on the findings, the study proposes training strategy optimizations to enhance student performance in baseball. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses machine learning to predict students’ scores in baseball training. The goal is to improve current teaching methods and provide personalized advice. Scientists trained different models using data like hitting and running times. They found that some models were better than others at predicting scores. The most important factors for success are how many hits and runs a student makes. Based on these findings, the study suggests ways to make training more effective. |
Keywords
» Artificial intelligence » Boosting » Machine learning » Optimization
