A decision stump is a machine learning model consisting of a one-level decision tree. [1] 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. [2]
Depending on the type of the input feature, several variations are possible. For nominal features, one may build a stump which contains a leaf for each possible feature value [3] [4] or a stump with the two leaves, one of which corresponds to some chosen category, and the other leaf to all the other categories. [5] For binary features these two schemes are identical. A missing value may be treated as a yet another category. [5]
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 [6] 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 with decision stumps as weak learners. [7]
The term "decision stump" was coined in a 1992 ICML paper by Wayne Iba and Pat Langley. [1] [8]
OneR
(for "1-rule").
DecisionStump
classifier.
These simple rules are in effect severely pruned decision trees and have been termed decision stumps Iba & Langley 1992
A decision stump is a machine learning model consisting of a one-level decision tree. [1] 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. [2]
Depending on the type of the input feature, several variations are possible. For nominal features, one may build a stump which contains a leaf for each possible feature value [3] [4] or a stump with the two leaves, one of which corresponds to some chosen category, and the other leaf to all the other categories. [5] For binary features these two schemes are identical. A missing value may be treated as a yet another category. [5]
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 [6] 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 with decision stumps as weak learners. [7]
The term "decision stump" was coined in a 1992 ICML paper by Wayne Iba and Pat Langley. [1] [8]
OneR
(for "1-rule").
DecisionStump
classifier.
These simple rules are in effect severely pruned decision trees and have been termed decision stumps Iba & Langley 1992