Overview
Boosting algorithms are a type of machine learning algorithm used in supervised learning. They are used to improve the accuracy of predictions by combining multiple weak models into a stronger one. Boosting algorithms can be used for classification, regression and other tasks, and can be applied to various datasets. They are particularly useful for datasets that are imbalanced or have outliers. Boosting algorithms are popular due to their ability to produce accurate models with relatively low computational cost and time, making them suitable for real-time applications.
Research published in this journal
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