I have a dataset that has continuous independent variables and a continuous dependent variable. The data has linear relationships, so a Linear Regression Model works perfectly well. However, I want to, for experimentation, model the data using a Decision Forest Regression and compare it against the Linear Regression output. I am using Azure Machine Learning Studio for this purpose.I get 30 decision trees constructed. Since we're dealing with continuous independent variables, each tree node/leaf is defined in terms of Variable1 < x or Variable1 > x.It looks like this: My Question:The target or dependent variable is also continuous. Values range from 60 to 100. In the picture you can see one path leading to value 75.2. When the trees are built, do they consider all the continuous values from 60 to 100 as discrete values and form these variable paths for each value? In other words, if I expand one of the trees from the decision forest, will I see all the possible continuous target values from the data appear in the tree as terminal leaf nodes?
Decision Forest Regression for continuous variables using Azure Machine Learning
regression;random forest;decision trees