Summary of Don’t Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning, by Andrea Apicella et al.
Don’t Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
by Andrea Apicella, Francesco Isgrò, Roberto Prevete
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning has transformed various domains by providing predictive capabilities. However, many practitioners lacking deep expertise adopt a “push the button” approach, utilizing user-friendly interfaces without understanding underlying algorithms. This approach provides convenience but raises concerns about outcome reliability, leading to challenges such as incorrect performance evaluation. This paper addresses data leakage in machine learning, where unintended information contaminates training data, impacting model performance evaluation. Users may overlook crucial steps, leading to optimistic estimates that don’t hold in real-world scenarios. Data leakage propagates through the ML workflow under certain conditions and affects specific tasks, including Transfer Learning. The connection between data leakage and task is investigated, comparing standard inductive ML with transductive ML frameworks. This paper categorizes data leakage in ML, emphasizing its importance for robust and reliable applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has made many things possible, but it can also be tricky. Some people use special tools to make predictions without fully understanding how they work. This can lead to mistakes when trying to measure how well the predictions are doing. A new study looks at a problem called data leakage, where extra information gets into the training data and affects how well the model performs. The study finds that data leakage happens more often than we think, especially with certain types of learning. It also shows how this can happen in different areas, like when using old data to train a new model. The researchers want us to be aware of this problem so we can make better predictions and avoid mistakes. |
Keywords
* Artificial intelligence * Machine learning * Transfer learning
