Ensemble methods are powerful techniques in artificial intelligence and machine learning that combine multiple models to improve the overall performance of predictive systems. Instead of relying on a single learning algorithm, ensemble methods integrate several models to produce better accuracy, robustness, and generalization. The basic idea behind ensemble learning is that a group of weak learners can come together to form a strong learner. A weak learner is a model that performs slightly better than random guessing, while a strong learner achieves high predictive performance. Ensemble methods reduce variance, bias, or improve predictions by aggregating the outputs of multiple models. They are widely used in both classification and regression tasks. One of the most common ensemble methods is bagging, also known as Bootstrap Aggregating. Bagging aims to reduce variance and improve stability by training multiple models on different subsets of the training data. These subsets are generated using bootstrap sampling, where samples are drawn randomly with replacement from the original dataset. Each model is trained independently using its own subset of data. After training, the predictions from all models are combined, usually through majority voting for classification tasks or averaging for regression tasks. Bagging is particularly useful for models that are sensitive to fluctuations in the training data, such as decision trees. By averaging the predictions of many trees, bagging reduces the risk of overfitting and improves generalization. A popular implementation of bagging is the Random Forest algorithm. Random Forest constructs a large number of decision trees during training and combines their outputs to make final predictions. In addition to using bootstrap sampling, Random Forest also introduces randomness in feature selection. At each split in a decision tree, only a random subset of features is considered instead of all features. This randomness helps create diverse trees that are less correlated with each other. As a result, the ensemble becomes more powerful and robust. Random Forest is widely used in machine learning because it handles high-dimensional data well, is resistant to overfitting, and provides good accuracy in many real-world applications. Another important ensemble method is boosting. Boosting focuses on converting weak learners into strong learners by training models sequentially. Unlike bagging, where models are trained independently, boosting trains each new model by giving more importance to the instances that were misclassified by previous models. In this way, the algorithm gradually improves its performance by focusing on difficult examples. The predictions of all models are then combined using weighted voting or summation. AdaBoost, or Adaptive Boosting, is one of the earliest and most influential boosting algorithms. In AdaBoost, each training instance is assigned a weight. Initially, all instances have equal