AdaBoost, short for Adaptive Boosting, is a popular machine learning algorithm that is used for classification tasks. It is an ensemble method that combines multiple weak classifiers to create a strong classifier. The basic idea behind AdaBoost is to iteratively train weak classifiers on different subsets of the training data, giving more weight to the misclassified instances in each iteration.
Here’s a high-level overview of how the AdaBoost algorithm works:
Initialize weights: Initially, each training instance is assigned an equal weight.
Train weak classifiers: In each iteration, a weak classifier is trained on the weighted training data. A weak classifier is a simple model that performs slightly better than random guessing.
Weighted error calculation: The weighted error is calculated as the sum of the weights of the misclassified instances.
Classifier weight calculation: The weight of the current weak classifier is determined based on its classification accuracy.
Update instance weights: The weights of the misclassified instances are increased, while the weights of the correctly classified instances are decreased.
Repeat: Steps 2 to 5 are repeated for a specified number of iterations or until a stopping criterion is met.
Final classifier creation: The final strong classifier is created by combining the weak classifiers, giving more weight to the more accurate ones.
Classification: To classify a new instance, each weak classifier predicts the class label and the final prediction is based on the weighted vote of the weak classifiers.
AdaBoost is effective in handling complex classification tasks and has been widely used in areas like face detection, object recognition, and natural language processing. It leverages the strength of multiple weak classifiers to achieve better overall accuracy than any individual weak classifier.
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