Mar 17, 2025 · 3 min read · AdaBoost (Adaptive Boosting) is an ensemble learning technique primarily used for classification problems. It combines multiple weak learners (typically decision trees with a single decision stump) to form a strong classifier. The models are trained ...
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Oct 9, 2024 · 14 min read · In previous parts of decision trees, we explored what are decision trees and how they can be used for regression as well as classification. In this blog we will see the problem with normal classification tree, and how to overcome it using various ens...
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Nov 17, 2021 · 6 min read · Boosting is one of the most widely used classes of algorithms in machine learning, applied globally to tackle a variety of complex problems. It involves combining multiple weak learners—typically simple models that perform just slightly better than r...
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