I'm looking at example solutions to Kaggle's Titantic competition. In short: Given passenger information such as age, sex, fare, class, can you predict whether or not they survived?
A lot of people like to preprocess the data by binning age and fare into ranges.
But then they throw a Random Forest at the problem, and I feel like binning is a waste of information here, because when building a decision tree, the algorithm kinda does the binning for you, based on the best split.
Is that intuition correct, or does the simplification achieved by binning make up for the loss of detailed information about the distribution?