Random Forests are an ensemble learning method primarily used for classification and regression that operates by constructing a multitude of decision trees during training and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. The "randomness" in Random Forests comes from two main sources: bootstrap aggregating (bagging), where each tree is trained on a random subset of the training data with replacement, and feature randomness, where at each split in a decision tree, only a random subset of features is considered. This ensemble approach helps to mitigate the overfitting issues inherent in single decision trees and reduces variance, leading to more robust and accurate predictions. Random Forests are highly versatile, can handle both numerical and categorical data, and are less prone to overfitting compared to individual decision trees, making them a popular choice in various real-world applications.