AdaBoost Classifier, Explained: A Visual Guide with Code Examples

AdaBoost is an ensemble machine learning model that uses a sequence of weighted decision trees to make predictions. It combines the trees through a weighted voting system, where better-performing trees have more influence in the final decision. The model's strength comes from its adaptive learning process, where each new tree focuses on and corrects the mistakes made by the previous trees. The algorithm starts by giving equal weight to each training example and then updates these weights based on the performance of each tree. This process is repeated for a specified number of trees, typically 50-100. The final prediction is made by combining all trees' votes, each weighted by its importance value.