Loading Now

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

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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