Random forest

A random forest is a type of machine learning model that is used to make predictions. It works by creating many decision trees, which are like flowcharts that ask a series of questions to figure out the answer to a problem. The random forest combines the answers from all of the decision trees to make a final prediction.

It’s called a “random” forest because it creates the decision trees using random samples of the data, which helps make the model more accurate and unbiased. This is because the model is not relying on any one specific decision tree, but rather on the combination of many different decision trees that were created using different random samples of the data.

Random forests are useful because they can handle a large amount of data and they can make very accurate predictions. They are often used to predict things like what kind of animal is in a picture or whether a person will like a movie.