Random Forest is an ensemble-type machine learning algorithm that combines multiple decision trees into a more robust and accurate prediction model. Random Forest uses a technique called "bagging" (bootstrap aggregating) to create multiple training samples from the original training data set, and each of these samples is used to train a decision tree. The individual decision trees are then combined into an overall model by using a weighted averaging technique.

Each decision tree in the Random Forest is trained on a random sample of features and training instances, allowing it to learn different patterns in different subsets of the dataset. When a prediction is made on a new instance, each Random Forest decision tree generates a prediction and the final prediction of the model is the weighted average of all the predictions of the individual trees.

Random Forest is known to be a robust and accurate machine learning technique, especially on data sets with high dimensionality and categorical features. The model is also able to handle missing values and noise in the data, and can provide information about the importance of features in the prediction task. Due to these advantages, Random Forest is used in a wide variety of applications, such as image classification, stock price prediction and fraud detection.