LogitBoost is an iterative boosting algorithm specifically designed for binary classification tasks. It is a variation of the general boosting framework that combines multiple weak classifiers to create a strong predictive model. The core principle is to adaptively focus on the instances that are most difficult to classify correctly by assigning higher weights to them during the boosting iterations. ### Key Features of LogitBoost: 1. **Objective**: LogitBoost aims to minimize the logistic loss function, which is appropriate for binary classification problems.
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