Summary of Be Aware Of Overfitting by Hyperparameter Optimization!, By Igor V. Tetko et al.
Be aware of overfitting by hyperparameter optimization!
by Igor V. Tetko, Ruud van Deursen, Guillaume Godin
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers investigate the effectiveness of hyperparameter optimization in machine learning models for predicting solubility. They use graph-based methods and compare different approaches, including varying data cleaning protocols and hyperparameter tuning. The authors find that hyperparameter optimization doesn’t always lead to better results due to overfitting when using traditional statistical measures. Interestingly, pre-set hyperparameters can achieve similar results with a significant reduction in computational effort (up to 10,000 times). Additionally, the paper introduces a novel representation learning method called Transformer CNN, which outperforms graph-based methods in 26 out of 28 pairwise comparisons, requiring only a fraction of the time. The study highlights the importance of using consistent statistical measures when evaluating calculation results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make machine learning models better for predicting solubility. The researchers tested different ways to find the best settings for their models and found that just using some pre-set settings could work almost as well, but much faster! They also developed a new way to learn representations called Transformer CNN, which performed really well compared to other methods. Overall, this study shows that we need to be careful when choosing how to compare our results, so we can get the best answers. |
Keywords
» Artificial intelligence » Cnn » Hyperparameter » Machine learning » Optimization » Overfitting » Representation learning » Transformer
