Decision stump
A decision stump is a decision tree of depth one with a single decision node and prediction leaves.
Grokking Machine Learning
A shallow decision tree consisting of a single feature-based split followed by leaf predictions.
- How it is used
- Uses one-question trees to classify examples or predict numerical labels before extending them into deeper decision trees.
- How deep it goes
- Defines the structure, traversal, leaf predictions, and split criteria, but stops short of deeper-tree construction for the stump itself.
- Examples
- Uses a jacket decision, app recommendation by age or platform, fraud screening, illness classification, and age-based engagement prediction.
- What you can do after
- Build it from scratch by selecting a question or cutoff, assigning leaf predictions, and evaluating the resulting classifier or regressor.
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Ensemble Methods for Machine Learning
A decision stump is a decision tree of depth one containing one decision node and two leaf nodes.
- How it is used
- Uses one-level trees as simple, efficient base estimators for sequential ensembles such as boosting.
- How deep it goes
- Defines the single-node structure and contrasts its simple functions with deeper trees, without detailing split-selection internals.
- Examples
- Uses axis-parallel tree partitions, weighted training examples, three successive stumps, and a combined boosted ensemble.
- What you can do after
- Apply it via boosting by training stumps sequentially and increasing attention to prior mistakes.
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Related concepts
- decision treekind of · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
- AdaBoostused by · AdaBoost is an ensemble-learning algorithm that sequentially combines weak learners, adaptively reweighting training examples so that later learners emphasize those earlier learners handled poorly.
- weak learnerkind of · A weak learner is a predictive model that performs only modestly better than chance or a simple baseline and can contribute to a stronger ensemble.
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