Summary of Superfast Selection For Decision Tree Algorithms, by Huaduo Wang and Gopal Gupta
Superfast Selection for Decision Tree Algorithms
by Huaduo Wang, Gopal Gupta
First submitted to arxiv on: 31 May 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 proposed Superfast Selection method reduces the time complexity for selecting optimal splits in decision trees and feature selection algorithms, making it more efficient when dealing with tabular data. The approach speeds up split selection by lowering the time complexity from O(MN) to O(M), where M is the number of input examples and N the number of unique values. This enhancement also eliminates the need for pre-encoding features for heterogeneity. By integrating Superfast Selection into the CART algorithm, the Ultrafast Decision Tree (UDT) is created, which can complete training in a single pass with a time complexity of O(KM^2). The Training Only Once Tuning further enables UDT to avoid repetitive hyperparameter tuning, achieving faster training times. Experimental results demonstrate that UDT can train on large datasets within seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Superfast Selection method is a new way to make decision trees and feature selection algorithms work faster. It helps by making it quicker to find the best place to split data into smaller groups. This makes it more efficient when working with big datasets. The approach also gets rid of the need for special encoding for features that have different types. By combining this method with the CART algorithm, a new decision tree called Ultrafast Decision Tree is created. It can learn from data very quickly and doesn’t need to repeat the learning process many times to find the best settings. This makes it much faster than other methods. |
Keywords
» Artificial intelligence » Decision tree » Feature selection » Hyperparameter
