Summary of Experimental Comparison Of Ensemble Methods and Time-to-event Analysis Models Through Integrated Brier Score and Concordance Index, by Camila Fernandez (lpsm) et al.
Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Index
by Camila Fernandez, Chung Shue Chen, Chen Pierre Gaillard, Alonso Silva
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 reviews and compares the performance of several prediction models for time-to-event analysis, including semi-parametric and parametric statistical models, as well as machine learning approaches. The study is conducted on three datasets and evaluated using two different scores: the integrated Brier score and concordance index. Ensemble methods are explored to improve prediction accuracy and robustness, with a simulation experiment evaluating factors influencing performance ranking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to best predict when something will happen, like when a machine might break or someone might stop using a product. It compares different ways of doing this, including some old statistical methods and newer machine learning approaches. The study uses three sets of data and checks the results against two different measurements: one that’s good for measuring accuracy and another that’s good for checking how well it works in real-life situations. The paper also looks at how combining these different approaches can make the predictions even better. |
Keywords
* Artificial intelligence * Machine learning
