Random Forest is a versatile machine learning method capable of performing both regression and classification tasks by constructing multiple decision trees. It is an ensemble technique that builds numerous trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random Forest addresses the limitations of individual decision trees by reducing overfitting and improving generalization. Each tree is trained on a random subset of the data, and a random subset of features is considered for splitting at each node, introducing diversity among the trees. This method is known for its high accuracy, robustness to overfitting, and ability to handle large datasets with high dimensionality. It is widely used in applications such as fraud detection, recommendation systems, and bioinformatics.