Random Survival Forests

Random Survival Forests is a statistical method used to analyze time-to-event data, such as how long patients survive after treatment. It builds multiple decision trees from random samples of data to make predictions about survival times. Each tree gives a vote on expected outcomes, and by combining these votes, the model provides robust estimates. This approach accounts for complex patterns and censoring—when the exact time of an event isn't known—making it particularly useful in medical research and other fields where understanding time until an event is crucial.