Regression targets where extrapolation may be needed. Decision tree methods cannot extrapolate, the predictions are have to be a mean of a subgroup of the data.
Consider: Predicting how much a customer might pay by end of month, with information we have at the start of the month.
In this example, if a customer had a record $10m of open invoices due by EoM and the largest payment amount received in prior months of $5m, the decision tree cannot possibly predict the payment amount will be ~$10m, even when the best feature indicates the payment will be $10m.
There are some hacks/techniques which can maybe reduce this issue, but they don't always work.
Consider: Predicting how much a customer might pay by end of month, with information we have at the start of the month.
In this example, if a customer had a record $10m of open invoices due by EoM and the largest payment amount received in prior months of $5m, the decision tree cannot possibly predict the payment amount will be ~$10m, even when the best feature indicates the payment will be $10m.
There are some hacks/techniques which can maybe reduce this issue, but they don't always work.