Summary of Variation in Prediction Accuracy Due to Randomness in Data Division and Fair Evaluation Using Interval Estimation, by Isao Goto
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation
by Isao Goto
First submitted to arxiv on: 2 Sep 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 addresses the challenge of building predictive models for diseases using machine learning algorithms by investigating the impact of dataset partitioning on model generalizability. The authors employed an autoML framework and open diabetes data to construct 33,600 diagnosis models with varying initial conditions, demonstrating that prediction accuracy is dependent on these conditions. By applying statistical interval estimation, the study provides a fair comparison of the accuracy of the models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem in using machine learning algorithms for disease prediction. Researchers have already made models for different diseases using big datasets and special algorithms, but there are still some issues with making these models work everywhere. One reason is that when we split up the data, it can make the models less useful. The study creates many diabetes diagnosis models using an automatic machine learning tool and a large diabetes dataset. It finds that how well the models predict depends on how they’re started. To compare these models fairly, the researchers use statistics to find the range of predicted accuracy. |
Keywords
* Artificial intelligence * Machine learning
