Summary of Binary Classification: Is Boosting Stronger Than Bagging?, by Dimitris Bertsimas and Vasiliki Stoumpou
Binary Classification: Is Boosting stronger than Bagging?
by Dimitris Bertsimas, Vasiliki Stoumpou
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Random Forests have been a popular choice for tabular data classification, but their simplicity has led to comparisons with more performant models like XGBoost. The proposed Enhanced Random Forests address these limitations by introducing adaptive sample and model weighting. An iterative algorithm adjusts training sample weights to prioritize harder examples, while personalized tree weighting schemes are developed for each new sample. The results show significant improvements over regular Random Forests across 15 binary classification datasets, outperforming XGBoost with default hyperparameters. The proposed methodology also enables importance scores for trees based on their contributions to classifying each new sample, recovering partial interpretability. This equivalence in performance and edge in interpretability highlights the potential of bagging methods like Enhanced Random Forests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random Forests are a type of machine learning algorithm that has been widely used for classification tasks. They work by combining many simple decision trees to make predictions. However, they have some limitations, such as not being able to handle very large datasets and not providing much information about why certain predictions were made. The new algorithm, Enhanced Random Forests, tries to address these issues by adjusting the weights of different samples in the training data and using personalized tree weights for each new sample. This allows the algorithm to prioritize harder examples and focus on a smaller number of trees that are most important for making predictions. The results show that this algorithm performs better than regular Random Forests and XGBoost, especially when the dataset is very large or complex. |
Keywords
» Artificial intelligence » Bagging » Classification » Machine learning » Xgboost
