 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.