Summary of Randomized-grid Search For Hyperparameter Tuning in Decision Tree Model to Improve Performance Of Cardiovascular Disease Classification, by Abhay Kumar Pathak et al.
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification
by Abhay Kumar Pathak, Mrityunjay Chaubey, Manjari Gupta
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF); Computation (stat.CO)
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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 A novel hybrid optimization method, called Randomized-Grid Search, is proposed to address the limitations of traditional hyperparameter tuning techniques in machine learning-based cardiovascular disease diagnosis. The approach combines the global exploration strengths of Random Search with the focused search of Grid Search in promising regions. This efficient balancing of exploration and exploitation optimizes the hyperparameters for a Decision Tree model applied to the UCI heart disease dataset for classification. Experimental results show that Randomized-Grid Search outperforms traditional methods by significant margins, enhancing model performance, accuracy, generalization, and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on developing smart systems to diagnose heart diseases using electronic health data and machine learning algorithms. They want to improve the process of finding the best settings for these algorithms so they can make more accurate predictions. To do this, they’re proposing a new way of searching for the best settings called Randomized-Grid Search. This approach combines the benefits of two other methods to find the right balance between exploring many possibilities and focusing on the most promising ones. The results show that this new method works better than traditional approaches, leading to more accurate diagnoses and better use of computer resources. |
Keywords
» Artificial intelligence » Classification » Decision tree » Generalization » Grid search » Hyperparameter » Machine learning » Optimization
