The prominent AI and machine learning-related problems are well-known by most people in the IT sector. Some of these include algorithmic biases, AI’s black box problem and privacy and consent-related issues. A slightly underrated machine learning problem is the suitability of certain algorithms to solve specific problems more than others. AdaBoost, a portmanteau for Adaptive Boosting, is an example of boosting in machine learning to improve algorithmic efficiency and capability.
What is AdaBoost?
Adaptive boosting is a type of data modeling technique known as ensemble learning, which involves using a multi-model approach to improve the learning capacity of algorithms and models. To give a simple example, consider a team of students learning a complex and multifaceted mathematical topic involving calculus, geometry, probability, linear programming and Boolean numbers. Assuming that each student has varying levels of understanding of basic mathematics, the topic is learned and, later, solved efficiently when the so-called “weakest” learner/s in the group learns a part of it. The second learner learns the parts that the first one couldn’t, and subsequently, each learner does the same. In this way, a large topic is learned by a collective of five learners. Similarly, AdaBoost, in simple terms, combines models and algorithms to gradually convert “weak learning algorithms” into strong ones.
How Does AdaBoost Improve Machine Learning?
The process of boosting in machine learning, or building models until the errors and deficiencies of previous ones are minimized to infinitesimal values, is used to improve the predictive abilities of algorithms and neural networks. As a result, the algorithms can identify and classify data provided to them with much more accuracy as compared to standard machine learning algorithms. Generally, AdaBoost involves decision trees that execute tasks in an iterative manner. These trees involve the provision of equal “weights” to multiple data points. The balance in weights allows AdaBoost algorithms to have improved classification compared to regular algorithms.
Simply put, AdaBoost and ensemble learning are vital for widening the scope of what machine learning algorithms can do. The technique can enable algorithms to solve a large array of problems and execute most business functions. Eventually, AdaBoost holds the key to eliminating problems such as biases and precision errors in AI applications.
