Adaptive Boosting

Definition of Adaptive Boosting

Adaptive boosting, also known as AdaBoost, is a machine learning algorithm used to improve the accuracy of a classification model. It works by iteratively training a series of weak classifiers on a dataset, and then combining their predictions to produce a more accurate final prediction.

What is Adaptive Boosting used for?

Adaptive Boosting, or AdaBoost, is a powerful machine learning algorithm used for classification problems. It works by combining several “weak” learners into a single strong learner that can accurately classify data points. Specifically, it combines multiple weak classifiers (often decision trees) to form a stronger classifier that performs better than any of the weak learners alone. It works by giving more importance to those data points which were incorrectly classified in the earlier rounds of training and thus makes it possible to learn from mistakes while building the ensemble. It has proven to be effective in many fields such as computer vision, speech recognition, and bioinformatics.

AdaBoost works by training a sequence of weak classifiers on different subsets of the dataset and combining them through a weighted majority vote. Each subsequent classifier takes into account the misclassifications made by the previous ones and creates an updated weight for each instance so that it will focus on those instances which are most likely to be misclassified in future rounds. This process is repeated until there are no more misclassifications, or until all the weights become equal (which usually means that all instances have been correctly classified). The final output is then determined by combining all these individual outcomes with their corresponding weights.

One major advantage of using AdaBoost over other machine learning algorithms is its ability to select only important features from large datasets. Furthermore, because it relies on weaker models rather than one strong model, it can be applied to problems which are too complex for simple algorithms like Decision Trees or Support Vector Machines. Finally, its ability to quickly adapt to changing situations makes it useful in applications where the data may change rapidly over time.