Summary of Probsaint: Probabilistic Tabular Regression For Used Car Pricing, by Kiran Madhusudhanan et al.
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
by Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme
First submitted to arxiv on: 6 Mar 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 The paper proposes a machine learning model called ProbSAINT, which offers a principled approach for uncertainty quantification of used car price predictions. The model provides both accurate point predictions and probability distributions, allowing it to flag predictions where the model is unsure. This approach can be used as a dynamic forecasting model for predicting price probabilities based on expected offer duration. The authors show that ProbSAINT outperforms state-of-the-art boosting techniques in certain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Used cars are becoming increasingly popular, and with the rise of online marketplaces, accurate pricing is crucial for fair transactions. However, current models struggle to quantify their uncertainty, which can lead to unreliable predictions. This paper introduces ProbSAINT, a new model that not only provides accurate price predictions but also shows how certain it is about those predictions. This allows buyers and sellers to make more informed decisions. |
Keywords
* Artificial intelligence * Boosting * Machine learning * Probability
