Boosting

Description

Boosting is an ensemble learning technique that iteratively improves weak classifiers by focusing on misclassified examples. Unlike bagging, it adjusts training example weights to enhance accuracy.

Advantages:

• Boosts weak classifiers' accuracy significantly.
• Easy to implement & widely applicable.
• Handles noisy data & reduces overfitting.

Disadvantages:

• Sensitive to outliers (risk of overfitting).
• Computationally expensive for large datasets.
• Hard to interpret, as it combines multiple classifiers.

Workflow

There are several boosting algorithms, but one of the most popular ones is AdaBoost (short for adaptive boosting). The AdaBoost algorithm works as follows:

1. Initialize equal weights for training examples.
2. Train a weak classifier.
3. Compute its weighted error rate.
4. Determine its importance based on error rate.
5. Increase weights of misclassified examples.
6. Normalize weights.
7. Repeat for a set number of iterations or until desired accuracy.
8. Combine weak classifiers into a strong model with weighted importance.