Random Forest is a machine learning algorithm used for supervised learning tasks such as classification and regression. It combines multiple decision trees to create a more accurate and stable prediction. Random Forest works by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. It is a powerful tool for predictive analytics and is widely used in maintenance operations and management. Random Forest is advantageous in Predictive Maintenance (PdM) because it manages outliers well, handles missing values, and is resistant to overfitting. It is also relatively easy to use and can be used to identify important features in a dataset.