Summary of Beyond Algorithm Hyperparameters: on Preprocessing Hyperparameters and Associated Pitfalls in Machine Learning Applications, by Christina Sauer et al.
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
by Christina Sauer, Anne-Laure Boulesteix, Luzia Hanßum, Farina Hodiamont, Claudia Bausewein, Theresa Ullmann
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 tackle the challenges faced by non-experts in generating and evaluating prediction models using supervised machine learning (ML). They highlight the importance of considering hyperparameter tuning, not just for algorithmic parameters but also for preprocessing steps that affect model performance. The authors review various procedures for generating and evaluating models, emphasizing the need to account for optimization choices to avoid exaggerated claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can be a powerful tool for making predictions. However, it’s not always easy to get good results, especially if you’re new to using machine learning algorithms. One problem is that people often forget that there are many ways to prepare data before feeding it into the algorithm. This preparation, or preprocessing, can affect how well the model works. The authors of this paper want to help people do better predictive modeling by showing them different ways to generate and evaluate models. They also highlight some common mistakes that people make when optimizing their models. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization » Supervised
