agging, or Bootstrap Aggregating, is an ensemble learning method that involves training multiple models on different random subsets of the training data and averaging their predictions to reduce variance and prevent overfitting. It is commonly used with decision trees, leading to techniques like Random Forests. Boosting is an ensemble learning technique that builds models sequentially, with each model correcting the errors of its predecessor, aiming to reduce bias and variance. It combines weak learners, often decision trees, into a strong predictive model, like in the case of AdaBoost or Gradient Boosting. Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to capture long-term dependencies and patterns in sequential data by using memory cells, gates, and loops. LSTMs are widely used in tasks involving time series, natural language processing, and speech recognition.