by regression tree on the ram price data the difference between the models is quite striking the linear model approximates the data with lineas we knew it would this line provides quite good forecast for the test data (the years after )while glossing over some of the finer variations in both the training and the test data the tree modelon the other handmakes perfect predictions on the training datawe did not restrict the complexity of the treeso it learned the whole dataset by heart howeveronce we leave the data range for which the model has datathe model simply keeps predicting the last known point the tree has no ability to generate "newresponsesoutside of what was seen in the training data this shortcoming applies to all models based on trees strengthsweaknessesand parameters as discussed earlierthe parameters that control model complexity in decision trees are the pre-pruning parameters that stop the building of the tree before it is fully developed usuallypicking one of the pre-pruning strategies--setting either it is actually possible to make very good forecasts with tree-based models (for examplewhen trying to predict whether price will go up or downthe point of this example was not to show that trees are bad model for time seriesbut to illustrate particular property of how trees make predictions supervised learning