AdaBoost in Machine Learning
adaboost is for classifications stumps are weak learners AdaBoost (Adaptive Boosting) is a popular ensemble learning technique that combines the outputs of several weak classifiers to create a strong classifier. Developed by Yoav Freund and Robert Schapire in 1996, AdaBoost works by iteratively training weak learners, typically decision stumps (simple decision trees with one split),…
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