Reduces bias and also variance
Boosting is an ensemble learning technique that combines multiple weak learners to create a strong learner. In boosting we iteratively learn classifiers and weak classifiers are used.
- Key Characteristics:
- Iteratively improves model performance by learning from previous models' mistakes
- Creates a sequence of models where each new model compensates for the weaknesses of previous ones
Boosting Methods