Gradient Boosting Machines  Algorithm Concepts of Gradient Boosting Machines Gradient Boosting Machines (GBM) is a machine learning algorithm that uses a sequence of decision trees to make predictions. It is similar to random forests in that it builds multiple decision trees, but unlike random forests, it builds each tree sequentially, using the errors from the previous tree to train the next one. GBM is a type of boosting algorithm, which means that it boosts the performance of weaker learners by combining them with strong learners. Gradient Boosting Machines Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., gradient boosting machines). Split the data into training and validation sets. Construct a series of weak learners, each attempting to correct the errors of the previous one. Aggregate the predictions from all the learners to make a final prediction. Regularize the model to avoid overfitting. Evaluate the model on the validation set to estimate its