Python Decision Tree Regressor Pruning
You can't; use matlab.  Struggling with this at the moment.  Using a python based home-cooked decision tree is also an option.  However, there is no guarantee it will work properly (lots of places you can screw up). And you need to implement with numpy if you want any kind of reasonable run-time (also struggling with this now).  
If you still have this problem, I do have a decision tree working with node knowledge and am implementing pruning this weekend...
If I get it to run fast and the code isn't too embarrassingly complicated, I will post a GitHub up here if you are still interested, in exchange for endorsements of ML'ing and Python/Numpy expertise on my LinkedIn.
I'm using scikit-learn to construct regression trees, using tree.DecisionTreeRegression().
I'm giving 56 data samples and it constructs me a Tree with 56 nodes (pruning=0).
How can I implement some pruning to the tree? Any help is appreciated!