Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit?
dt_ap = tree.DecisionTreeClassifier(random_state=1, max_depth=13)
boosted_dt = AdaBoostClassifier(dt_ap, random_state=1)
boosted_dt.fit(X_train, Y_train)
Is this equivalent of pruning a decision tree?
Though they have similar goals (i.e. placing some restrictions to the model so that it doesn't grow very complex and overfit), max_depth isn't equivalent to pruning. The way pruning usually works is that go back through the tree and replace branches that do not help with leaf nodes.
If not, how could I prune a decision tree using scikit?
You can't through scikit-learn (without altering the source code).
Quote taken from the Decision Tree documentation: Mechanisms such as pruning (not currently supported)
If you want to post-prune a tree you have to do it on your own:
You can read this excellent post detailing how to do so.
Now with the new version 0.22.1, you can! It does pruning based on minimal cost-complexity pruning: the subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen.
https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html
