Decision stump

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An example of a decision stump that discriminates between two of three classes of Iris flower data set: Iris versicolor and Iris virginica. The petal width is in centimetres. This particular stump achieves 94% accuracy on the Iris dataset for these two classes.

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

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]

See also

References

  1. ^
    ISBN 978-1-55860-247-2
    .
  2. S2CID 6596
    .
  3. ISBN 978-0-596-51649-9. Archived from the original
    on 2010-06-18. Retrieved 2010-06-10.
  4. Weka
    under the name OneR (for "1-rule").
  5. ^
    Weka
    's DecisionStump classifier.
  6. S2CID 2483269
    .
  7. S2CID 2796017
    .
  8. ISBN 3-540-57868-4. These simple rules are in effect severely pruned decision trees and have been termed decision stumps Iba & Langley 1992
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