Summary of The Effect Of Different Feature Selection Methods on Models Created with Xgboost, by Jorge Neyra et al.
The effect of different feature selection methods on models created with XGBoost
by Jorge Neyra, Vishal B. Siramshetty, Huthaifa I. Ashqar
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 study investigates the impact of various feature selection methods on XGBoost, a renowned machine learning algorithm known for its exceptional regularization capabilities. It reveals that three distinct techniques for reducing dimensionality yield no statistically significant changes in model prediction accuracy. This finding challenges the conventional notion that noise reduction is essential to prevent overfitting, suggesting that XGBoost may be an exception. However, this approach could still prove useful for reducing computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how different ways to reduce features affect models made with XGBoost. It finds that three methods of doing this don’t make a significant difference in how well the model predicts things. This is surprising because people thought that getting rid of noisy data was important to prevent overfitting, but it seems that XGBoost might be an exception. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Overfitting » Regularization » Xgboost
