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A decision stump is a
A decision stump is a machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
model consisting of a one-level decision tree
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
. That is, it is a decision tree with one internal node (the root) which is immediately connected to the terminal nodes (its leaves). A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are also called 1-rules.
Depending on the type of the input feature
Feature may refer to:
Computing
* Feature (CAD), could be a hole, pocket, or notch
* Feature (computer vision), could be an edge, corner or blob
* Feature (software design) is an intentional distinguishing characteristic of a software item ...
, several variations are possible. For nominal features, one may build a stump which contains a leaf for each possible feature value or a stump with the two leaves, one of which corresponds to some chosen category, and the other leaf to all the other categories.This is what has been implemented in Weka
The weka, also known as the Māori hen or woodhen (''Gallirallus australis'') is a flightless bird species of the rail family. It is endemic to New Zealand. It is the only extant member of the genus ''Gallirallus''. Four subspecies are recognize ...
's DecisionStump classifier. For binary features these two schemes are identical. A missing value may be treated as a yet another category.
For continuous features, usually, some threshold feature value is selected, and the stump contains two leaves — for values below and above the threshold. However, rarely, multiple thresholds may be chosen and the stump therefore contains three or more leaves.
Decision stumps are often used as components (called "weak learners" or "base learners") in machine learning ensemble techniques such as bagging and boosting. For example, a Viola–Jones face detection algorithm employs AdaBoost
AdaBoost, short for ''Adaptive Boosting'', is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many other types of ...
with decision stumps as weak learners.
The term "decision stump" was coined in a 1992 ICML
The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artifici ...
paper by Wayne Iba and Pat Langley.
See also
*Decision list Decision lists are a representation for Boolean functions which can be easily learnable from examples. Single term decision lists are more expressive than disjunctions and conjunctions; however, 1-term decision lists are less expressive than the ...
References
{{DEFAULTSORT:Decision Stump Decision trees