Intermediate · Data
Attribute sampling
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Attribute sampling trains decision trees using only random subsets of features at each node, improving efficiency and diversity.
Technical Definition
Attribute sampling trains decision trees using only random subsets of features at each node, improving efficiency and diversity.
How it works
Attribute sampling is a technique used in training ensemble models like decision forests. Instead of considering all available features when deciding how to split a node in a decision tree, only a random subset of features is evaluated. This process introduces randomness, which can lead to more diverse trees and potentially better generalization.
Related Concepts
- Feature Selection — Choosing the most useful subset of features to improve performance and interpretability.
- Ensemble learning — Combining multiple machine learning models to improve overall prediction accuracy and robustness.
- Random forest — An ensemble machine learning method that builds multiple decision trees to improve prediction accuracy and control overfitting.
- Decision tree — A decision tree is a supervised learning model that uses a tree-like structure of decisions and their possible consequences.