Pre-pruning: provide a max_depth, max_leafnodes, or min_samples_leaf parameters of the tree to stop the growth early
clf2 = DecisionTreeClassifier(max_depth = 3).fit(X_train, y_train)

Post-pruning/pruning: build a complete tree with all pure leaves then prune back the tree into a simpler form; this is not available in SciKit Learn
Controlling Decision Tree Complexity

University of Michigan - Ann Arbor

1) Although trees may be easier to understand and interpret, they do not provide the same level of predictive accuracy compared to traditional regression and classification methods

2) Small changes in data can cause large changes in the final estimated tree

3) Not fit for continuous variables - While working with continuous numerical variables, decision tree loses information, when it categorizes variables in different categories.

4) Overfitting - Can create over-complex trees that do not generalize the data well and suffer from high variance. This problem gets solved by setting constraints on model parameters and pruning.


Disadvantages to Using Decision Trees

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

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