204.3.10 Pruning a Decision Tree in Python

Taking care of complexity of Decision Tree and solving the problem of overfitting.

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  • Growing the tree beyond a certain level of complexity leads to overfitting.
  • In our data, age doesn’t have any impact on the target variable.
  • Growing the tree beyond Gender is not going to add any value. Need to cut it at Gender.
  • This process of trimming trees is called Pruning.

Pruning to Avoid Overfitting

  • Pruning helps us to avoid overfitting
  • Generally it is preferred to have a simple model, it avoids overfitting issue
  • Any additional split that does not add significant value is not worth while.
  • We can avoid overfitting by changing the parameters like
    • max_leaf_nodes
    • min_samples_leaf
    • max_depth
  • Pruning Parameters
    • max_leaf_nodes
      • Reduce the number of leaf nodes
    • min_samples_leaf
      • Restrict the size of sample leaf
      • Minimum sample size in terminal nodes can be fixed to 30, 100, 300 or 5% of total
    • max_depth
      • Reduce the depth of the tree to build a generalized tree
      • Set the depth of the tree to 3, 5, 10 depending after verification on test data


Code-Tree Pruning

#We will rebuild a new tree by using above data and see how it works by tweeking the parameteres

dtree = tree.DecisionTreeClassifier(criterion = "gini", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= 5),y_train)
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,
            max_features=None, max_leaf_nodes=10, min_samples_leaf=5,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='random')
predict3 = dtree.predict(X_train)
[1 1 0 0 0 1 1 1 1 0 0 1 0 0]
predict4 = dtree.predict(X_test)
[1 1 0 0 0 1]
#Accuracy of the model that we created with modified model parameters.
score2 = dtree.score(X_test, y_test)

The next post is a practice session on tree building model selection.

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