AdaBoost

AdaBoost

AdaBoost, short for Adaptive boosting, is a popular ensemble learning algorithm used for both classification and regression tasks. For classification, it combines multiple weak classifiers into a strong classifier, improving predictive performance. A weak learner is a classifier that performs only slightly better than random guessing. AdaBoost assigns higher weights to misclassified instances during each iteration, allowing the subsequent weak learners to focus on the previously misclassified samples. By iteratively adjusting the weights, AdaBoost improves the performance of the overall model.

Working of AdaBoost

Following is the step-by-step implementation of the AdaBoost algorithm for classification.

1. Initialize the training dataset weights: We assign equal weights to each instance in the training dataset. Initially, all instances are considered equally important.

2. Train a base learner on the weighted training dataset: The base learner is typically a weak model (e.g., a decision tree with limited depth). The model is trained to minimize the weighted error, focusing on the misclassified instances.

3. Calculate the weighted error of the base learner: We calculate the error of the base learner by comparing its predictions with the true labels. We increase the weight of misclassified instances to give them higher importance in the next iteration.

4. Calculate the base learner’s weight in the ensemble model: We determine the weight of the base learner based on its performance. The better the performance (lower weighted error), the higher the weight assigned.

5. Update the training dataset weights: We increase the weights of the misclassified instances to focus on them in the next iteration. This allows subsequent base learners to pay more attention to challenging cases. We normalize the weights so that they sum up to 1. This step ensures that the weights remain within a valid range.

6. Create the ensemble model: We combine the base learners into a final ensemble model. The base learners are weighted according to their performance (e.g., weighted voting or weighted averaging). The weights determine the influence of each base learner in the ensemble. The ensemble model represents the final boosted model that combines the predictions of multiple base learners. It can predict new instances by aggregating ...