Definition: Boosting also combines predictions from multiple models, but in a sequential manner. Unlike bagging, boosting focuses on giving more weight to instances that the current set of models misclassifies, aiming to correct errors iteratively.

Key Steps:

  1. Model Training:
    • Train a base model on the original dataset, and assign weights to each data point. Initially, all weights are equal.
  2. Weight Adjustment:
    • Increase the weights of misclassified instances, making them more influential in the next round of training. This allows subsequent models to focus on the previously misclassified data.
  3. Sequential Model Building:
    • Train additional models sequentially, with each model giving more attention to the instances that were misclassified by the previous models.
  4. Combining Predictions:
    • Combine the predictions of all models, giving more weight to models that performed well on their respective training subsets.

Example: AdaBoost : AdaBoost is a popular boosting algorithm that combines weak learners to create a strong learner. It assigns higher weights to misclassified instances, leading subsequent models to focus on these instances.

Advantages of Bagging:

  • Reduces overfitting by introducing diversity through multiple models.
  • Enhances model robustness by averaging out errors or biases present in individual models.
  • Particularly effective when the base model is sensitive to the specific data it is trained on.

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