Develop an AI system to solve a real-world problem : Boosting and Ensembles

The video tutorial introduces ensemble methods, which use multiple models to improve prediction accuracy. It explains the benefits of ensembles, such as flexibility and reduced overfitting. The concept of boosting is introduced, focusing on training models on data points that previous models got wrong. The tutorial demonstrates using Adaboost and random forests to enhance accuracy, highlighting the difference between training and testing accuracy and addressing overfitting issues.

Describe the process of how new models are added to an ensemble.

How does the Adaboost classifier work in the context of ensemble methods?

What is the significance of the overfitting gap in model training?