Boosting is a machine learning algorithm technique that involves combining weak models into a strong model. It works by training a series of models sequentially, with each model attempting to correct the errors of the previous models. In this way, boosting can improve the overall accuracy of a model, making it more accurate than any individual model in the series. Boosting is an iterative process where each subsequent model is trained on a modified version of the training set, where examples that were incorrectly classified by the previous models are given a higher weight. The idea is to focus on the examples that were difficult to classify by the previous models and to force the subsequent models to pay more attention to these examples. By doing so, the subsequent models can learn from the mistakes of the previous models and improve the overall performance of the model.

Ensemble Boosting is a machine learning technique that combines multiple weak learners (models that perform slightly better than random guessing) to create a strong learner that can make accurate predictions. The goal of boosting is to sequentially train a set of weak models and combine them into a strong model that can accurately classify or predict new data. The general idea of boosting is to iteratively adjust the weights of training examples and train a sequence of weak classifiers (e.g., decision trees, SVMs, etc.) to improve their accuracy in predicting the target variable. Boosting focuses on the examples that are difficult to classify correctly and gives more weight to those examples in each iteration. By doing so, the model focuses on those examples and eventually achieves a high level of accuracy.